Fit an ARIMA(p, d, q) model to every ticker’s price series in the Toolkit instance and forecast forecast_steps periods ahead.

Also known as: Box-Jenkins model, autoregressive integrated moving average.

An ARIMA(p, d, q) model differences the series d times to remove a (stochastic) trend, then fits an autoregressive-moving-average model to the result – see time_series_model.get_arima_forecast for the full formula, estimation method (exact Maximum Likelihood via the Kalman filter) and its practical caveats.

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Calculate the ARIMA Forecast in Python

The ARIMA Forecast is available in the Econometrics module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_arima_forecast as shown below.

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.econometrics.get_arima_forecast(period="quarterly")

Which returns:

Step AAPL MSFT
1 138.273 243.199
2 147.039 250.190
3 154.992 257.106
4 162.323 264.014
5 169.180 270.922

Parameters

get_arima_forecast accepts the following parameters:

  • period (str, optional): The data frequency (daily, weekly, monthly, quarterly, or yearly). Defaults to “daily”.
  • column (str, optional): The historical data column to fit. Defaults to “Adj Close”.
  • p (int, optional): The autoregressive order. Defaults to 1.
  • d (int, optional): The number of times to difference the series. Defaults to 1 (the typical choice for a non-stationary price level series).
  • q (int, optional): The moving-average order. Defaults to 1.
  • forecast_steps (int, optional): The number of periods ahead to forecast. Defaults to 5.
  • include_constant (bool, optional): Whether to estimate a free intercept. Defaults to True – see time_series_model.get_arima_forecast for when to set this to False.
  • include_benchmark (bool, optional): Whether to include “Benchmark” among the tickers forecast. Defaults to False.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
VAR Forecast VECM Forecast Impulse Response Function (IRF) Variance Decomposition

The Econometrics module page introduces the module, and the sidebar lists all of its functions.

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