ARIMA Modeling in Python: A Quick Start Guide
ARIMA models remain a cornerstone of classical time series analysis. Python’s statsmodels package makes it straightforward to specify, fit, and evaluate these models.
1. Identifying the ARIMA Order
Plot the autocorrelation (ACF) and partial autocorrelation (PACF) to determine suitable values for the AR (p) and MA (q) terms. Differencing can help stabilize non-stationary series before fitting.
2. Fitting the Model
With parameters chosen, use statsmodels.tsa.arima.model.ARIMA to estimate the coefficients. Review summary statistics to ensure reasonable residual behavior.
3. Forecast Evaluation
Evaluate predictions using metrics like mean absolute error (MAE) or root mean squared error (RMSE). Cross-validation on rolling windows helps confirm that the model generalizes well.
While ARIMA is a classical technique, it remains a powerful baseline and a stepping stone toward more complex forecasting methods.
