Multivariate statistical details

Multivariate statistical details describe Measures of error and model fit.

Mean Absolute Error (MAE): Computed as the average absolute difference between the values fitted by the model (one-step ahead in-sample forecast), and the observed historical data.
Mean Absolute Percent Error (MAPE): The average absolute percent difference between the values that are fitted by the model and the observed data values.
Mean Absolute Scaled Error (MASE): The error measure that is used for model accuracy. It is the MAE divided by the MAE of the naive model. The naive model is one that predicts the value at time point t as the previous historical value. Scaling by this error means that you can evaluate how good the model is compared to the naive model. If the MASE is greater than 1, then the model is worse than the naive model. The lower the MASE, the better the model is compared to the naive model.
Root Mean Squared Error (RMSE): The square root of the MSE. It is on the same scale as the observed data values. Mean Squared Error (MSE): The sum of squared difference between the values that are fitted by the model and observed values that are divided by the number of historical points, minus the number of parameters in the model. The number of parameters in the model is subtracted from the number of historical points to be consistent with an unbiased model variance estimate.
Accuracy: The estimated predictive accuracy of the forecast.