The difference between ARIMA and Random Forest in time series forecasting comes down to model type, assumptions, and how they handle data:
In short: ARIMA is a traditional statistical model suited for linear, Stationary Time Series, while Random Forest is a flexible machine learning model that can handle non-linear patterns and many predictors.
| Aspect | ARIMA / SARIMA | Random Forest |
|---|---|---|
| Model type | Parametric, statistical time series model | Non-parametric, ensemble machine learning model |
| Assumptions | Assumes linear relationships; requires stationary data (constant mean/variance over time) | Makes no linearity or stationarity assumptions; learns patterns from features |
| Input | Primarily past values (lags) of the series | Can use lagged values, rolling statistics, external features, or any predictor |
| Captures patterns | Linear trends and seasonality | Non-linear patterns, interactions between features |
| Interpretability | Relatively interpretable (coefficients have meaning) | Less interpretable; feature importance can be analyzed |
| Forecast horizon | Good for short- to medium-term forecasts if assumptions hold | Can perform well for complex patterns and longer horizons if features are engineered well |
| Use case | Classic time series forecasting where data is seasonal/trended | When time series is influenced by multiple variables or shows non-linear behavior |