How do I evaluate time series models effectively?

How do I evaluate time series models effectively?

To evaluate time series models, you can use metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared.

What is the difference between MAE and MSE?

MAE measures the average magnitude of errors without considering their direction, while MSE squares the errors, giving more weight to larger errors.

When should I use R-squared for evaluation?

R-squared is useful for understanding the proportion of variance explained by the model, but it can be misleading for non-linear models.

Are there any visual methods to evaluate time series models?

Yes, you can use residual plots, ACF/PACF plots, and time series decomposition to visually assess model performance.