Evaluating Forecast Accuracy (MAPE, MAD) Evaluating forecast accuracy is crucial for any time series forecasting model. We compare the actual values to the p...
Evaluating forecast accuracy is crucial for any time series forecasting model. We compare the actual values to the predicted values and calculate how well the model performs in terms of forecasting accuracy.
Metrics for accuracy:
Mean Absolute Error (MAE): The average absolute difference between the actual and predicted values. Lower MAE indicates better model accuracy.
Mean Absolute Percentage Error (MAPE): The average absolute difference expressed as a percentage of the actual value. Lower MAPE indicates better model accuracy.
Mean Absolute Error Squared (MAD): The average squared difference between the actual and predicted values. Lower MAD indicates better model accuracy.
How to calculate MAPE and MAD:
Calculate the difference between the actual and predicted values for each time step.
Calculate the average of these differences.
Convert the average difference to a percentage.
Interpreting results:
Lower MAPE, MAE, and MAD: The model performs better in terms of forecasting accuracy.
Higher MAPE, MAE, and MAD: The model performs worse in terms of forecasting accuracy.
Examples:
Imagine you have a time series of daily closing prices of a stock. A low MAPE and MAE suggest that the model accurately predicts the stock price based on past values.
Conversely, a high MAD suggests that the model struggles to accurately predict the stock price and its daily fluctuations.
Further considerations:
While MAPE and MAD are widely used, they may not be the most appropriate metrics for all forecasting problems. Consider other metrics such as Mean Squared Error (MSE) or Root Mean Squared Error (RMSE) depending on the problem context.
Choosing the right metric depends on the specific characteristics of the time series and the forecasting problem at hand