Time-series forecasting is a specific type of forecasting / predictive modeling that uses historical data to predict future trends in a particular time series. There are several different metrics that can be used to measure the accuracy and efficacy of a time-series forecasting model, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and others. By understanding these performance metrics, you can better assess the effectiveness of your time-series forecasting model and make necessary adjustments as needed. In this blog, you will learn about the different time-series forecasting model performance metrics and how to use them for model evaluation. Check out a related post – Different types of time-series forecasting models
There are several different performance metrics that can be used to measure the accuracy and efficacy of a time-series forecasting model, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and others. By understanding these performance metrics, you can better assess the effectiveness of your time-series forecasting model and make necessary adjustments as needed.
MSE is the mean squared error between the actual and predicted values, while MAE is the mean absolute error between the actual and predicted values. MSE is a more accurate measure of forecasting error than MAE, as it takes into account the magnitude of the errors. MSE penalizes the large deviation between the actual and predicted value.
In this blog post, you have learned about the different performance metrics used for evaluating time-series forecasting models. You have also learned about the importance of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), and how to calculate them. By understanding these metrics, you can better assess the accuracy of your time-series forecasting model and make necessary adjustments as needed.
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