ARIMA Forecast

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.

ARIMA Forecast in Python

get_arima_forecast is part of 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:

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.

Related Time Series Forecasting