How do I evaluate the performance of a time-series forecasting model using RMSE and MAE

Question

With the help of proper python programming can you tell me How do I evaluate the performance of a time-series forecasting model using RMSE and MAE?

Answer 1

To evaluate a time-series forecasting model, compute Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) using sklearn.metrics, where RMSE penalizes large errors more, while MAE measures absolute differences, both providing insight into model accuracy.

Here is the code snippet given below:

In the above code we are using the following techniques:

• RMSE (sqrt(mean_squared_error)):

  • Penalizes large errors more due to squaring.
  • Useful for applications where outliers matter (e.g., finance, weather forecasting).

• MAE (mean_absolute_error):

  • Measures average absolute differences between predicted and actual values.
  • More interpretable and robust to outliers compared to RMSE.

• Comparison & Selection:

  • If outliers are important → RMSE (e.g., stock price forecasting).
  • If you want a balanced measure → MAE (e.g., energy consumption forecasting).

Hence, using RMSE and MAE together provides a comprehensive evaluation of a time-series forecasting model, where RMSE emphasizes large errors, and MAE gives an overall absolute deviation.