.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/forecasting.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_forecasting.py>`
        to download the full example code or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_forecasting.py:


Forecasting Financial Statements with ``finstmt``
=================================================

   Note: The plots are not coming through and the formatting of the
   statements is off in the web view. Download the Jupyter notebook for
   the full experience.

First import the necessary classes and ``pandas``.

.. GENERATED FROM PYTHON SOURCE LINES 12-19

.. code-block:: default


    import os
    os.chdir('..')
    from finstmt import FinancialStatements, IncomeStatements, BalanceSheets
    import pandas as pd









.. GENERATED FROM PYTHON SOURCE LINES 20-23

Load Financial Statements
-------------------------


.. GENERATED FROM PYTHON SOURCE LINES 23-37

.. code-block:: default


    root_folder = os.path.sep.join(['..', 'tests', 'sources', 'stockrow', 'CAT'])

    inc_path = os.path.join(root_folder, 'annual_income.csv')
    bs_path = os.path.join(root_folder, 'annual_bs.csv')

    inc_df = pd.read_csv(inc_path, index_col=0)
    bs_df = pd.read_csv(bs_path, index_col=0)

    bs = BalanceSheets.from_df(bs_df)
    inc = IncomeStatements.from_df(inc_df)
    stmts = FinancialStatements(inc, bs)






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /home/runner/work/py-finstmt/py-finstmt/finstmt/findata/period_data.py:116: UserWarning: Previously had ebit extracted from "Operating Income". Replacing with value from "EBIT"
      warnings.warn(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/findata/period_data.py:116: UserWarning: Previously had ebit extracted from "Operating Income". Replacing with value from "EBIT"
      warnings.warn(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/findata/period_data.py:116: UserWarning: Previously had ebit extracted from "Operating Income". Replacing with value from "EBIT"
      warnings.warn(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/findata/period_data.py:116: UserWarning: Previously had ebit extracted from "Operating Income". Replacing with value from "EBIT"
      warnings.warn(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/findata/period_data.py:116: UserWarning: Previously had ebit extracted from "Operating Income". Replacing with value from "EBIT"
      warnings.warn(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/findata/period_data.py:116: UserWarning: Previously had ebit extracted from "Operating Income". Replacing with value from "EBIT"
      warnings.warn(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/findata/period_data.py:116: UserWarning: Previously had ebit extracted from "Operating Income". Replacing with value from "EBIT"
      warnings.warn(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/findata/period_data.py:116: UserWarning: Previously had ebit extracted from "Operating Income". Replacing with value from "EBIT"
      warnings.warn(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/findata/period_data.py:116: UserWarning: Previously had ebit extracted from "Operating Income". Replacing with value from "EBIT"
      warnings.warn(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/findata/period_data.py:116: UserWarning: Previously had ebit extracted from "Operating Income". Replacing with value from "EBIT"
      warnings.warn(




.. GENERATED FROM PYTHON SOURCE LINES 38-41

View Existing Statements
~~~~~~~~~~~~~~~~~~~~~~~~


.. GENERATED FROM PYTHON SOURCE LINES 41-45

.. code-block:: default


    stmts







.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">

            <h2>Income Statement</h2>
            <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>12/31/2009</th>
          <th>12/31/2010</th>
          <th>12/31/2011</th>
          <th>12/31/2012</th>
          <th>12/31/2013</th>
          <th>12/31/2014</th>
          <th>12/31/2015</th>
          <th>12/31/2016</th>
          <th>12/31/2017</th>
          <th>12/31/2018</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>Revenue</th>
          <td>$32,396,000,000</td>
          <td>$42,588,000,000</td>
          <td>$60,138,000,000</td>
          <td>$65,875,000,000</td>
          <td>$55,656,000,000</td>
          <td>$55,184,000,000</td>
          <td>$47,011,000,000</td>
          <td>$38,537,000,000</td>
          <td>$45,462,000,000</td>
          <td>$54,722,000,000</td>
        </tr>
        <tr>
          <th>Cost of Goods Sold</th>
          <td>$24,931,000,000</td>
          <td>$31,281,000,000</td>
          <td>$44,404,000,000</td>
          <td>$47,852,000,000</td>
          <td>$41,454,000,000</td>
          <td>$41,342,000,000</td>
          <td>$34,133,000,000</td>
          <td>$28,640,000,000</td>
          <td>$31,906,000,000</td>
          <td>$37,719,000,000</td>
        </tr>
        <tr>
          <th>Gross Profit</th>
          <td>$7,465,000,000</td>
          <td>$11,307,000,000</td>
          <td>$15,734,000,000</td>
          <td>$18,023,000,000</td>
          <td>$14,202,000,000</td>
          <td>$13,842,000,000</td>
          <td>$12,878,000,000</td>
          <td>$9,897,000,000</td>
          <td>$13,556,000,000</td>
          <td>$17,003,000,000</td>
        </tr>
        <tr>
          <th>SG&amp;A Expense</th>
          <td>$3,645,000,000</td>
          <td>$4,248,000,000</td>
          <td>$5,203,000,000</td>
          <td>$5,919,000,000</td>
          <td>$5,547,000,000</td>
          <td>$6,529,000,000</td>
          <td>$4,951,000,000</td>
          <td>$4,383,000,000</td>
          <td>$4,999,000,000</td>
          <td>$5,478,000,000</td>
        </tr>
        <tr>
          <th>Interest Expense</th>
          <td>$389,000,000</td>
          <td>$343,000,000</td>
          <td>$396,000,000</td>
          <td>$467,000,000</td>
          <td>$465,000,000</td>
          <td>$484,000,000</td>
          <td>$507,000,000</td>
          <td>$505,000,000</td>
          <td>$531,000,000</td>
          <td>$404,000,000</td>
        </tr>
        <tr>
          <th>Income Tax Expense</th>
          <td>$-270,000,000</td>
          <td>$968,000,000</td>
          <td>$1,720,000,000</td>
          <td>$2,528,000,000</td>
          <td>$1,319,000,000</td>
          <td>$692,000,000</td>
          <td>$916,000,000</td>
          <td>$192,000,000</td>
          <td>$3,339,000,000</td>
          <td>$1,698,000,000</td>
        </tr>
        <tr>
          <th>R&amp;D Expense</th>
          <td>$1,421,000,000</td>
          <td>$1,905,000,000</td>
          <td>$2,297,000,000</td>
          <td>$2,466,000,000</td>
          <td>$2,046,000,000</td>
          <td>$2,380,000,000</td>
          <td>$2,119,000,000</td>
          <td>$1,853,000,000</td>
          <td>$1,842,000,000</td>
          <td>$1,850,000,000</td>
        </tr>
        <tr>
          <th>Depreciation &amp; Amortization Expense</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Other Operating Expenses</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Gain on Sale of Investments</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Gain on Sale of Assets</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Impairment Expense</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Operating Expense</th>
          <td>$6,888,000,000</td>
          <td>$7,344,000,000</td>
          <td>$8,581,000,000</td>
          <td>$9,450,000,000</td>
          <td>$8,574,000,000</td>
          <td>$10,528,000,000</td>
          <td>$9,093,000,000</td>
          <td>$8,735,000,000</td>
          <td>$9,096,000,000</td>
          <td>$8,710,000,000</td>
        </tr>
        <tr>
          <th>Earnings Before Interest and Taxes</th>
          <td>$1,014,000,000</td>
          <td>$4,011,000,000</td>
          <td>$7,044,000,000</td>
          <td>$8,676,000,000</td>
          <td>$5,573,000,000</td>
          <td>$3,628,000,000</td>
          <td>$3,935,000,000</td>
          <td>$630,000,000</td>
          <td>$4,624,000,000</td>
          <td>$8,249,000,000</td>
        </tr>
        <tr>
          <th>Earnings Before Tax</th>
          <td>$625,000,000</td>
          <td>$3,668,000,000</td>
          <td>$6,648,000,000</td>
          <td>$8,209,000,000</td>
          <td>$5,108,000,000</td>
          <td>$3,144,000,000</td>
          <td>$3,428,000,000</td>
          <td>$125,000,000</td>
          <td>$4,093,000,000</td>
          <td>$7,845,000,000</td>
        </tr>
        <tr>
          <th>Net Income</th>
          <td>$895,000,000</td>
          <td>$2,700,000,000</td>
          <td>$4,928,000,000</td>
          <td>$5,681,000,000</td>
          <td>$3,789,000,000</td>
          <td>$2,452,000,000</td>
          <td>$2,512,000,000</td>
          <td>$-67,000,000</td>
          <td>$754,000,000</td>
          <td>$6,147,000,000</td>
        </tr>
      </tbody>
    </table>
    </div>
            <h2>Balance Sheet</h2>
            <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>12/31/2009</th>
          <th>12/31/2010</th>
          <th>12/31/2011</th>
          <th>12/31/2012</th>
          <th>12/31/2013</th>
          <th>12/31/2014</th>
          <th>12/31/2015</th>
          <th>12/31/2016</th>
          <th>12/31/2017</th>
          <th>12/31/2018</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>Cash and Cash Equivalents</th>
          <td>$4,867,000,000</td>
          <td>$3,592,000,000</td>
          <td>$3,057,000,000</td>
          <td>$5,490,000,000</td>
          <td>$6,081,000,000</td>
          <td>$7,341,000,000</td>
          <td>$6,460,000,000</td>
          <td>$7,168,000,000</td>
          <td>$8,261,000,000</td>
          <td>$7,857,000,000</td>
        </tr>
        <tr>
          <th>Short-Term Investments</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Cash and Short-Term Investments</th>
          <td>$4,867,000,000</td>
          <td>$3,592,000,000</td>
          <td>$3,057,000,000</td>
          <td>$5,490,000,000</td>
          <td>$6,081,000,000</td>
          <td>$7,341,000,000</td>
          <td>$6,460,000,000</td>
          <td>$7,168,000,000</td>
          <td>$8,261,000,000</td>
          <td>$7,857,000,000</td>
        </tr>
        <tr>
          <th>Receivables</th>
          <td>$27,162,000,000</td>
          <td>$28,849,000,000</td>
          <td>$30,803,000,000</td>
          <td>$33,911,000,000</td>
          <td>$33,499,000,000</td>
          <td>$32,772,000,000</td>
          <td>$30,507,000,000</td>
          <td>$29,088,000,000</td>
          <td>$30,725,000,000</td>
          <td>$31,899,000,000</td>
        </tr>
        <tr>
          <th>Inventory</th>
          <td>$6,360,000,000</td>
          <td>$9,587,000,000</td>
          <td>$14,544,000,000</td>
          <td>$15,547,000,000</td>
          <td>$12,625,000,000</td>
          <td>$12,205,000,000</td>
          <td>$9,700,000,000</td>
          <td>$8,614,000,000</td>
          <td>$10,018,000,000</td>
          <td>$11,529,000,000</td>
        </tr>
        <tr>
          <th>Deferred Tax Assets, Current</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Other Current Assets</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Total Current Assets</th>
          <td>$27,217,000,000</td>
          <td>$31,810,000,000</td>
          <td>$37,900,000,000</td>
          <td>$42,138,000,000</td>
          <td>$38,335,000,000</td>
          <td>$38,867,000,000</td>
          <td>$33,508,000,000</td>
          <td>$31,967,000,000</td>
          <td>$36,244,000,000</td>
          <td>$38,603,000,000</td>
        </tr>
        <tr>
          <th>Gross Property, Plant &amp; Equipment</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Accumulated Depreciation</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Net Property, Plant &amp; Equipment</th>
          <td>$12,386,000,000</td>
          <td>$12,539,000,000</td>
          <td>$14,395,000,000</td>
          <td>$16,461,000,000</td>
          <td>$17,075,000,000</td>
          <td>$16,577,000,000</td>
          <td>$16,090,000,000</td>
          <td>$15,322,000,000</td>
          <td>$14,155,000,000</td>
          <td>$13,574,000,000</td>
        </tr>
        <tr>
          <th>Goodwill and Intangible Assets</th>
          <td>$2,734,000,000</td>
          <td>$3,419,000,000</td>
          <td>$11,448,000,000</td>
          <td>$10,958,000,000</td>
          <td>$10,552,000,000</td>
          <td>$9,770,000,000</td>
          <td>$9,436,000,000</td>
          <td>$8,369,000,000</td>
          <td>$8,311,000,000</td>
          <td>$8,114,000,000</td>
        </tr>
        <tr>
          <th>Long-Term Investments</th>
          <td>$105,000,000</td>
          <td>$164,000,000</td>
          <td>$133,000,000</td>
          <td>$272,000,000</td>
          <td>$272,000,000</td>
          <td>$257,000,000</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Deferred Tax Assets, Long-Term</th>
          <td>$3,930,000,000</td>
          <td>$3,424,000,000</td>
          <td>$3,737,000,000</td>
          <td>$3,558,000,000</td>
          <td>$2,147,000,000</td>
          <td>$3,143,000,000</td>
          <td>$2,489,000,000</td>
          <td>$2,790,000,000</td>
          <td>$1,693,000,000</td>
          <td>$1,439,000,000</td>
        </tr>
        <tr>
          <th>Other Long-Term Assets</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Total Non-Current Assets</th>
          <td>$32,821,000,000</td>
          <td>$32,210,000,000</td>
          <td>$43,318,000,000</td>
          <td>$46,832,000,000</td>
          <td>$46,561,000,000</td>
          <td>$45,814,000,000</td>
          <td>$44,834,000,000</td>
          <td>$42,737,000,000</td>
          <td>$40,718,000,000</td>
          <td>$39,906,000,000</td>
        </tr>
        <tr>
          <th>Total Assets</th>
          <td>$60,038,000,000</td>
          <td>$64,020,000,000</td>
          <td>$81,218,000,000</td>
          <td>$88,970,000,000</td>
          <td>$84,896,000,000</td>
          <td>$84,681,000,000</td>
          <td>$78,342,000,000</td>
          <td>$74,704,000,000</td>
          <td>$76,962,000,000</td>
          <td>$78,509,000,000</td>
        </tr>
        <tr>
          <th>Payables</th>
          <td>$2,993,000,000</td>
          <td>$5,856,000,000</td>
          <td>$8,161,000,000</td>
          <td>$6,753,000,000</td>
          <td>$6,560,000,000</td>
          <td>$6,515,000,000</td>
          <td>$5,023,000,000</td>
          <td>$4,614,000,000</td>
          <td>$6,487,000,000</td>
          <td>$7,051,000,000</td>
        </tr>
        <tr>
          <th>Short-Term Debt</th>
          <td>$9,784,000,000</td>
          <td>$7,981,000,000</td>
          <td>$9,648,000,000</td>
          <td>$12,391,000,000</td>
          <td>$11,031,000,000</td>
          <td>$11,501,000,000</td>
          <td>$12,844,000,000</td>
          <td>$13,965,000,000</td>
          <td>$11,031,000,000</td>
          <td>$11,553,000,000</td>
        </tr>
        <tr>
          <th>Current Portion of Long-Term Debt</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Tax Liabilities, Short-Term</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Other Current Liabilities</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Total Current Liabilities</th>
          <td>$18,975,000,000</td>
          <td>$22,020,000,000</td>
          <td>$28,357,000,000</td>
          <td>$29,415,000,000</td>
          <td>$27,297,000,000</td>
          <td>$27,877,000,000</td>
          <td>$26,242,000,000</td>
          <td>$26,132,000,000</td>
          <td>$26,931,000,000</td>
          <td>$28,218,000,000</td>
        </tr>
        <tr>
          <th>Long-Term Debt</th>
          <td>$21,847,000,000</td>
          <td>$20,437,000,000</td>
          <td>$24,944,000,000</td>
          <td>$27,752,000,000</td>
          <td>$26,719,000,000</td>
          <td>$27,784,000,000</td>
          <td>$25,169,000,000</td>
          <td>$22,818,000,000</td>
          <td>$23,847,000,000</td>
          <td>$25,000,000,000</td>
        </tr>
        <tr>
          <th>Total Debt</th>
          <td>$31,631,000,000</td>
          <td>$28,418,000,000</td>
          <td>$34,592,000,000</td>
          <td>$40,143,000,000</td>
          <td>$37,750,000,000</td>
          <td>$39,285,000,000</td>
          <td>$38,013,000,000</td>
          <td>$36,783,000,000</td>
          <td>$34,878,000,000</td>
          <td>$36,553,000,000</td>
        </tr>
        <tr>
          <th>Deferred Revenue</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Tax Liabilities, Long-Term</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Deposit Liabilities</th>
          <td>$1,217,000,000</td>
          <td>$1,831,000,000</td>
          <td>$2,487,000,000</td>
          <td>$2,638,000,000</td>
          <td>$2,360,000,000</td>
          <td>$1,697,000,000</td>
          <td>$1,146,000,000</td>
          <td>$1,167,000,000</td>
          <td>$1,426,000,000</td>
          <td>$1,243,000,000</td>
        </tr>
        <tr>
          <th>Other Long-Term Liabilities</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Total Non-Current Liabilities</th>
          <td>$31,763,000,000</td>
          <td>$30,675,000,000</td>
          <td>$39,459,000,000</td>
          <td>$41,973,000,000</td>
          <td>$36,721,000,000</td>
          <td>$39,978,000,000</td>
          <td>$37,215,000,000</td>
          <td>$35,359,000,000</td>
          <td>$36,265,000,000</td>
          <td>$36,211,000,000</td>
        </tr>
        <tr>
          <th>Total Liabilities</th>
          <td>$50,738,000,000</td>
          <td>$52,695,000,000</td>
          <td>$67,816,000,000</td>
          <td>$71,388,000,000</td>
          <td>$64,018,000,000</td>
          <td>$67,855,000,000</td>
          <td>$63,457,000,000</td>
          <td>$61,491,000,000</td>
          <td>$63,196,000,000</td>
          <td>$64,429,000,000</td>
        </tr>
        <tr>
          <th>Common Stock</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Other Comprehensive Income</th>
          <td>$-3,764,000,000</td>
          <td>$-4,051,000,000</td>
          <td>$-6,328,000,000</td>
          <td>$-6,433,000,000</td>
          <td>$-3,898,000,000</td>
          <td>$-6,431,000,000</td>
          <td>$-2,035,000,000</td>
          <td>$-2,039,000,000</td>
          <td>$-1,192,000,000</td>
          <td>$-1,684,000,000</td>
        </tr>
        <tr>
          <th>Retained Earnings</th>
          <td>$19,711,000,000</td>
          <td>$21,384,000,000</td>
          <td>$25,219,000,000</td>
          <td>$29,558,000,000</td>
          <td>$31,854,000,000</td>
          <td>$33,887,000,000</td>
          <td>$29,246,000,000</td>
          <td>$27,377,000,000</td>
          <td>$26,301,000,000</td>
          <td>$30,427,000,000</td>
        </tr>
        <tr>
          <th>Minority Interest</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Total Stockholder's Equity</th>
          <td>$8,740,000,000</td>
          <td>$10,824,000,000</td>
          <td>$12,883,000,000</td>
          <td>$17,532,000,000</td>
          <td>$20,811,000,000</td>
          <td>$16,746,000,000</td>
          <td>$14,809,000,000</td>
          <td>$13,213,000,000</td>
          <td>$13,766,000,000</td>
          <td>$14,080,000,000</td>
        </tr>
        <tr>
          <th>Total Liabilities and Equity</th>
          <td>$59,478,000,000</td>
          <td>$63,519,000,000</td>
          <td>$80,699,000,000</td>
          <td>$88,920,000,000</td>
          <td>$84,829,000,000</td>
          <td>$84,601,000,000</td>
          <td>$78,266,000,000</td>
          <td>$74,704,000,000</td>
          <td>$76,962,000,000</td>
          <td>$78,509,000,000</td>
        </tr>
        <tr>
          <th>Net Working Capital</th>
          <td>$30,529,000,000</td>
          <td>$32,580,000,000</td>
          <td>$37,186,000,000</td>
          <td>$42,705,000,000</td>
          <td>$39,564,000,000</td>
          <td>$38,462,000,000</td>
          <td>$35,184,000,000</td>
          <td>$33,088,000,000</td>
          <td>$34,256,000,000</td>
          <td>$36,377,000,000</td>
        </tr>
      </tbody>
    </table>
    </div>
        
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 46-53

Copy the Statements
~~~~~~~~~~~~~~~~~~~

To set up for running multiple forecasts off the same data, you can make
a copy of it using ``stmts.copy()``. Then any adjustments to the
configuration will only be in the original object and not the copy.


.. GENERATED FROM PYTHON SOURCE LINES 53-57

.. code-block:: default


    stmts2 = stmts.copy()









.. GENERATED FROM PYTHON SOURCE LINES 58-61

Run a Forecast
--------------


.. GENERATED FROM PYTHON SOURCE LINES 64-71

Set the Default Method
~~~~~~~~~~~~~~~~~~~~~~

This is not a necessary step, but this is the way to change the default
forecast method. The default is already ``cagr`` and so this doesn’t
actually have an effect.


.. GENERATED FROM PYTHON SOURCE LINES 71-75

.. code-block:: default


    stmts.config.update_all(['forecast_config', 'method'], 'cagr')









.. GENERATED FROM PYTHON SOURCE LINES 76-82

View the Forecast Assumptions
-----------------------------

All the assumptions going into the forecast are in
``forecast_assumptions``:


.. GENERATED FROM PYTHON SOURCE LINES 82-86

.. code-block:: default


    stmts.forecast_assumptions







.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>Method</th>
          <th>% of</th>
          <th>Cap</th>
          <th>Floor</th>
          <th>Plug</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>Revenue</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Cost of Goods Sold</th>
          <td>cagr</td>
          <td>revenue</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>SG&amp;A Expense</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Interest Expense</th>
          <td>cagr</td>
          <td>total_debt</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Income Tax Expense</th>
          <td>cagr</td>
          <td>ebt</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>R&amp;D Expense</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Cash and Cash Equivalents</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>True</td>
        </tr>
        <tr>
          <th>Receivables</th>
          <td>cagr</td>
          <td>revenue</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Inventory</th>
          <td>cagr</td>
          <td>revenue</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Net Property, Plant &amp; Equipment</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Goodwill and Intangible Assets</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Long-Term Investments</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Deferred Tax Assets, Long-Term</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Payables</th>
          <td>cagr</td>
          <td>revenue</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Short-Term Debt</th>
          <td>cagr</td>
          <td>total_debt</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Long-Term Debt</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>True</td>
        </tr>
        <tr>
          <th>Deposit Liabilities</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Other Comprehensive Income</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Retained Earnings</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 87-94

Run the First Forecast
~~~~~~~~~~~~~~~~~~~~~~

Forecasts are run using ``.forecast()`` and produce
``ForecastedFinancialStatements``. It is usually good practice to first
run a forecast, then view the results, then adjust as needed.


.. GENERATED FROM PYTHON SOURCE LINES 94-98

.. code-block:: default


    fcst = stmts.forecast()






.. rst-class:: sphx-glr-script-out

 .. code-block:: none


      0%|          | 0/16 [00:00<?, ?it/s]/home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:39: UserWarning: CAGR not an appropriate method for Income Tax Expense as y_0 is negative. Setting to 0 growth (recent value forecast)
      warnings.warn(message)
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()

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      0%|          | 0/38 [00:00<?, ?it/s]/home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:39: UserWarning: CAGR not an appropriate method for Long-Term Investments as y_T is 0. Setting to 0 growth (recent value forecast)
      warnings.warn(message)
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:39: UserWarning: CAGR not an appropriate method for Other Comprehensive Income as y_0 is negative, y_T is negative. Setting to 0 growth (recent value forecast)
      warnings.warn(message)
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()

    100%|##########| 38/38 [00:00<00:00, 409.19it/s]
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()




.. GENERATED FROM PYTHON SOURCE LINES 99-104

Note: Warnings are raised because CAGR is the default method but it
   is not suitable for items which begin with 0

Now view the forecast.


.. GENERATED FROM PYTHON SOURCE LINES 104-108

.. code-block:: default


    fcst







.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">

            <h2>Income Statement</h2>
            <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>12/31/2019</th>
          <th>12/31/2020</th>
          <th>12/31/2021</th>
          <th>12/31/2022</th>
          <th>12/31/2023</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>Revenue</th>
          <td>$57,667,220,438</td>
          <td>$60,770,957,074</td>
          <td>$64,041,741,490</td>
          <td>$67,488,564,448</td>
          <td>$71,120,900,608</td>
        </tr>
        <tr>
          <th>Cost of Goods Sold</th>
          <td>$39,313,545,944</td>
          <td>$40,975,500,271</td>
          <td>$42,707,712,625</td>
          <td>$44,513,153,118</td>
          <td>$46,394,917,421</td>
        </tr>
        <tr>
          <th>Gross Profit</th>
          <td>$18,353,674,493</td>
          <td>$19,795,456,804</td>
          <td>$21,334,028,865</td>
          <td>$22,975,411,330</td>
          <td>$24,725,983,186</td>
        </tr>
        <tr>
          <th>SG&amp;A Expense</th>
          <td>$5,705,772,841</td>
          <td>$5,943,016,376</td>
          <td>$6,190,124,394</td>
          <td>$6,447,507,055</td>
          <td>$6,715,591,574</td>
        </tr>
        <tr>
          <th>Interest Expense</th>
          <td>$415,740,590</td>
          <td>$418,147,270</td>
          <td>$421,436,796</td>
          <td>$425,677,315</td>
          <td>$430,948,096</td>
        </tr>
        <tr>
          <th>Income Tax Expense</th>
          <td>$2,236,447,584</td>
          <td>$2,485,650,525</td>
          <td>$2,753,182,973</td>
          <td>$3,040,236,677</td>
          <td>$3,348,074,638</td>
        </tr>
        <tr>
          <th>R&amp;D Expense</th>
          <td>$1,899,457,118</td>
          <td>$1,950,236,402</td>
          <td>$2,002,373,198</td>
          <td>$2,055,903,797</td>
          <td>$2,110,865,460</td>
        </tr>
        <tr>
          <th>Depreciation &amp; Amortization Expense</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Other Operating Expenses</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Gain on Sale of Investments</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Gain on Sale of Assets</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Impairment Expense</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Operating Expense</th>
          <td>$7,605,229,959</td>
          <td>$7,893,252,778</td>
          <td>$8,192,497,592</td>
          <td>$8,503,410,852</td>
          <td>$8,826,457,034</td>
        </tr>
        <tr>
          <th>Earnings Before Interest and Taxes</th>
          <td>$10,748,444,535</td>
          <td>$11,902,204,025</td>
          <td>$13,141,531,274</td>
          <td>$14,472,000,479</td>
          <td>$15,899,526,152</td>
        </tr>
        <tr>
          <th>Earnings Before Tax</th>
          <td>$10,332,703,944</td>
          <td>$11,484,056,756</td>
          <td>$12,720,094,478</td>
          <td>$14,046,323,164</td>
          <td>$15,468,578,056</td>
        </tr>
        <tr>
          <th>Net Income</th>
          <td>$8,096,256,360</td>
          <td>$8,998,406,230</td>
          <td>$9,966,911,505</td>
          <td>$11,006,086,487</td>
          <td>$12,120,503,417</td>
        </tr>
      </tbody>
    </table>
    </div>
            <h2>Balance Sheet</h2>
            <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>12/31/2019</th>
          <th>12/31/2020</th>
          <th>12/31/2021</th>
          <th>12/31/2022</th>
          <th>12/31/2023</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>Cash and Cash Equivalents</th>
          <td>$8,341,327,471</td>
          <td>$8,755,400,920</td>
          <td>$9,198,171,321</td>
          <td>$9,671,573,612</td>
          <td>$10,177,696,618</td>
        </tr>
        <tr>
          <th>Short-Term Investments</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Cash and Short-Term Investments</th>
          <td>$8,341,327,471</td>
          <td>$8,755,400,920</td>
          <td>$9,198,171,321</td>
          <td>$9,671,573,612</td>
          <td>$10,177,696,618</td>
        </tr>
        <tr>
          <th>Receivables</th>
          <td>$32,415,938,592</td>
          <td>$32,941,254,423</td>
          <td>$33,475,083,248</td>
          <td>$34,017,563,025</td>
          <td>$34,568,833,947</td>
        </tr>
        <tr>
          <th>Inventory</th>
          <td>$12,235,594,949</td>
          <td>$12,985,496,033</td>
          <td>$13,781,357,418</td>
          <td>$14,625,995,942</td>
          <td>$15,522,401,082</td>
        </tr>
        <tr>
          <th>Deferred Tax Assets, Current</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Other Current Assets</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Total Current Assets</th>
          <td>$52,992,861,012</td>
          <td>$54,682,151,376</td>
          <td>$56,454,611,986</td>
          <td>$58,315,132,579</td>
          <td>$60,268,931,647</td>
        </tr>
        <tr>
          <th>Gross Property, Plant &amp; Equipment</th>
          <td>$13,698,894,523</td>
          <td>$13,824,938,203</td>
          <td>-</td>
          <td>$14,080,515,420</td>
          <td>$14,210,070,397</td>
        </tr>
        <tr>
          <th>Accumulated Depreciation</th>
          <td>-</td>
          <td>-</td>
          <td>$13,952,141,611</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Net Property, Plant &amp; Equipment</th>
          <td>$13,698,894,523</td>
          <td>$13,824,938,203</td>
          <td>$13,952,141,611</td>
          <td>$14,080,515,420</td>
          <td>$14,210,070,397</td>
        </tr>
        <tr>
          <th>Goodwill and Intangible Assets</th>
          <td>$9,046,459,697</td>
          <td>$10,086,077,526</td>
          <td>$11,245,168,084</td>
          <td>$12,537,461,160</td>
          <td>$13,978,264,368</td>
        </tr>
        <tr>
          <th>Long-Term Investments</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Deferred Tax Assets, Long-Term</th>
          <td>$1,301,450,391</td>
          <td>$1,177,048,729</td>
          <td>$1,064,538,241</td>
          <td>$962,782,286</td>
          <td>$870,752,872</td>
        </tr>
        <tr>
          <th>Other Long-Term Assets</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Total Non-Current Assets</th>
          <td>$24,046,804,611</td>
          <td>$25,088,064,457</td>
          <td>$26,261,847,936</td>
          <td>$27,580,758,866</td>
          <td>$29,059,087,638</td>
        </tr>
        <tr>
          <th>Total Assets</th>
          <td>$77,039,665,624</td>
          <td>$79,770,215,833</td>
          <td>$82,716,459,922</td>
          <td>$85,895,891,445</td>
          <td>$89,328,019,285</td>
        </tr>
        <tr>
          <th>Payables</th>
          <td>$7,681,837,474</td>
          <td>$8,369,114,590</td>
          <td>$9,117,880,879</td>
          <td>$9,933,637,642</td>
          <td>$10,822,378,370</td>
        </tr>
        <tr>
          <th>Short-Term Debt</th>
          <td>$12,042,329,141</td>
          <td>$12,268,514,671</td>
          <td>$12,524,772,010</td>
          <td>$12,814,230,760</td>
          <td>$13,140,492,951</td>
        </tr>
        <tr>
          <th>Current Portion of Long-Term Debt</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Tax Liabilities, Short-Term</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Other Current Liabilities</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Total Current Liabilities</th>
          <td>$19,724,166,615</td>
          <td>$20,637,629,260</td>
          <td>$21,642,652,889</td>
          <td>$22,747,868,402</td>
          <td>$23,962,871,321</td>
        </tr>
        <tr>
          <th>Long-Term Debt</th>
          <td>$25,976,778,901</td>
          <td>$26,381,226,925</td>
          <td>$26,847,240,522</td>
          <td>$27,380,905,350</td>
          <td>$27,989,231,332</td>
        </tr>
        <tr>
          <th>Total Debt</th>
          <td>$38,019,108,042</td>
          <td>$38,649,741,596</td>
          <td>$39,372,012,532</td>
          <td>$40,195,136,110</td>
          <td>$41,129,724,283</td>
        </tr>
        <tr>
          <th>Deferred Revenue</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Tax Liabilities, Long-Term</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Deposit Liabilities</th>
          <td>$1,245,630,357</td>
          <td>$1,248,266,280</td>
          <td>$1,250,907,780</td>
          <td>$1,253,554,871</td>
          <td>$1,256,207,563</td>
        </tr>
        <tr>
          <th>Other Long-Term Liabilities</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Total Non-Current Liabilities</th>
          <td>$27,222,409,258</td>
          <td>$27,629,493,205</td>
          <td>$28,098,148,303</td>
          <td>$28,634,460,221</td>
          <td>$29,245,438,895</td>
        </tr>
        <tr>
          <th>Total Liabilities</th>
          <td>$46,946,575,873</td>
          <td>$48,267,122,465</td>
          <td>$49,740,801,191</td>
          <td>$51,382,328,623</td>
          <td>$53,208,310,216</td>
        </tr>
        <tr>
          <th>Common Stock</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Other Comprehensive Income</th>
          <td>$-1,684,000,000</td>
          <td>$-1,684,000,000</td>
          <td>$-1,684,000,000</td>
          <td>$-1,684,000,000</td>
          <td>$-1,684,000,000</td>
        </tr>
        <tr>
          <th>Retained Earnings</th>
          <td>$31,777,094,255</td>
          <td>$33,187,094,333</td>
          <td>$34,659,658,352</td>
          <td>$36,197,562,372</td>
          <td>$37,803,705,632</td>
        </tr>
        <tr>
          <th>Minority Interest</th>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
          <td>-</td>
        </tr>
        <tr>
          <th>Total Stockholder's Equity</th>
          <td>$30,093,094,255</td>
          <td>$31,503,094,333</td>
          <td>$32,975,658,352</td>
          <td>$34,513,562,372</td>
          <td>$36,119,705,632</td>
        </tr>
        <tr>
          <th>Total Liabilities and Equity</th>
          <td>$77,039,670,128</td>
          <td>$79,770,216,798</td>
          <td>$82,716,459,543</td>
          <td>$85,895,890,995</td>
          <td>$89,328,015,849</td>
        </tr>
        <tr>
          <th>Net Working Capital</th>
          <td>$36,969,696,068</td>
          <td>$37,557,635,866</td>
          <td>$38,138,559,787</td>
          <td>$38,709,921,325</td>
          <td>$39,268,856,659</td>
        </tr>
      </tbody>
    </table>
    </div>
        
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 109-111

And the plot:


.. GENERATED FROM PYTHON SOURCE LINES 111-115

.. code-block:: default


    fcst.plot()






.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <Figure size 1500x2100 with 19 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 116-119

It is also possible to plot a subset. Let’s focus on only a few items
for sake of example.


.. GENERATED FROM PYTHON SOURCE LINES 119-123

.. code-block:: default


    fcst.plot(subset=['revenue', 'cogs', 'cash'])






.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <Figure size 1500x300 with 3 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 124-130

Change Methods and Re-Run Forecast
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

After viewing the plots, the appropriate methods for forecasting each
item should be chosen. Then they can be updated.


.. GENERATED FROM PYTHON SOURCE LINES 130-135

.. code-block:: default


    stmts.config.update('revenue', ['forecast_config', 'method'], 'trend')
    stmts.config.update('cogs', ['forecast_config', 'method'], 'mean')









.. GENERATED FROM PYTHON SOURCE LINES 136-138

Now re-run the forecast.


.. GENERATED FROM PYTHON SOURCE LINES 138-142

.. code-block:: default


    fcst = stmts.forecast()






.. rst-class:: sphx-glr-script-out

 .. code-block:: none


      0%|          | 0/16 [00:00<?, ?it/s]/home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:39: UserWarning: CAGR not an appropriate method for Income Tax Expense as y_0 is negative. Setting to 0 growth (recent value forecast)
      warnings.warn(message)
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()

    100%|##########| 16/16 [00:00<00:00, 398.93it/s]

      0%|          | 0/38 [00:00<?, ?it/s]/home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:39: UserWarning: CAGR not an appropriate method for Long-Term Investments as y_T is 0. Setting to 0 growth (recent value forecast)
      warnings.warn(message)
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:39: UserWarning: CAGR not an appropriate method for Other Comprehensive Income as y_0 is negative, y_T is negative. Setting to 0 growth (recent value forecast)
      warnings.warn(message)
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()

    100%|##########| 38/38 [00:00<00:00, 464.03it/s]
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()




.. GENERATED FROM PYTHON SOURCE LINES 143-145

And plot the results:


.. GENERATED FROM PYTHON SOURCE LINES 145-149

.. code-block:: default


    fcst.plot(subset=['revenue', 'cogs', 'cash'])






.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <Figure size 1500x300 with 3 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 150-166

Now the methods for those forecasts has been updated.

Adjusting an Existing Forecast
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

While it is possible to use the ``manual`` forecast type and pass in the
desired growth rates or levels from the beginning, it is often more
convenient to first run the forecast, then simply adjust it. The
``.to_manual`` method of the forecast was added for this purpose.

Either ``adjustments`` or ``replacements`` can be passed while updating,
and either the level of the item or the growth in the item can be used
for the new manual forecast. For either one, a dictionary where the keys
are the index of the period and values are the adjustment/replacement or
a list containing all the adjustment/replacements can be used.


.. GENERATED FROM PYTHON SOURCE LINES 166-172

.. code-block:: default


    fcst.forecasts['cash'].to_manual(adjustments={0: 0.4})  # boost first forecast period cash growth by 40%
    fcst.forecasts['cogs'].to_manual(use_levels=True, replacements=[0.85 for _ in range(5)])  # use 85% of sales for full COGS forecast
    fcst.forecasts['revenue'].to_manual(use_levels=True, replacements={1: 8e10})  # set second forecast period revenue to 80,000,000,000






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()




.. GENERATED FROM PYTHON SOURCE LINES 173-176

Then just run the forecast again to get everything updated in the
statements. Then you can view the plots:


.. GENERATED FROM PYTHON SOURCE LINES 176-181

.. code-block:: default


    fcst = stmts.forecast()
    fcst.plot(subset=['revenue', 'cogs', 'cash'])






.. rst-class:: sphx-glr-script-out

 .. code-block:: none


      0%|          | 0/16 [00:00<?, ?it/s]/home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:39: UserWarning: CAGR not an appropriate method for Income Tax Expense as y_0 is negative. Setting to 0 growth (recent value forecast)
      warnings.warn(message)
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()

    100%|##########| 16/16 [00:00<00:00, 472.75it/s]

      0%|          | 0/38 [00:00<?, ?it/s]/home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:39: UserWarning: CAGR not an appropriate method for Long-Term Investments as y_T is 0. Setting to 0 growth (recent value forecast)
      warnings.warn(message)
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:39: UserWarning: CAGR not an appropriate method for Other Comprehensive Income as y_0 is negative, y_T is negative. Setting to 0 growth (recent value forecast)
      warnings.warn(message)
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/cagr.py:94: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
      self.result_df = past_df.append(future_df).sort_index()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()

    100%|##########| 38/38 [00:00<00:00, 462.42it/s]
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:72: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
      return pd.date_range(
    /home/runner/work/py-finstmt/py-finstmt/finstmt/forecast/models/base.py:39: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
      return pd.Series()

    <Figure size 1500x300 with 3 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 182-189

A Second Forecast
-----------------

Since we earlier did a ``deepcopy`` of the ``stmts`` object, that still
has the original forecast assumptions and can be used for a separate
forecast.


.. GENERATED FROM PYTHON SOURCE LINES 189-190

.. code-block:: default


    stmts2.forecast_assumptions




.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>Method</th>
          <th>% of</th>
          <th>Cap</th>
          <th>Floor</th>
          <th>Plug</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>Revenue</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Cost of Goods Sold</th>
          <td>cagr</td>
          <td>revenue</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>SG&amp;A Expense</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Interest Expense</th>
          <td>cagr</td>
          <td>total_debt</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Income Tax Expense</th>
          <td>cagr</td>
          <td>ebt</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>R&amp;D Expense</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Cash and Cash Equivalents</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>True</td>
        </tr>
        <tr>
          <th>Receivables</th>
          <td>cagr</td>
          <td>revenue</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Inventory</th>
          <td>cagr</td>
          <td>revenue</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Net Property, Plant &amp; Equipment</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Goodwill and Intangible Assets</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Long-Term Investments</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Deferred Tax Assets, Long-Term</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Payables</th>
          <td>cagr</td>
          <td>revenue</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Short-Term Debt</th>
          <td>cagr</td>
          <td>total_debt</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Long-Term Debt</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>True</td>
        </tr>
        <tr>
          <th>Deposit Liabilities</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Other Comprehensive Income</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
        <tr>
          <th>Retained Earnings</th>
          <td>cagr</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>False</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  44.582 seconds)


.. _sphx_glr_download_auto_examples_forecasting.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/nickderobertis/py-finstmt/gh-pages?urlpath=lab/tree/notebooks/auto_examples/forecasting.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: forecasting.py <forecasting.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: forecasting.ipynb <forecasting.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_