![]() ![]() The crucial point is that MAPE puts much more weight on extreme values and positive errors, which makes MASE a favor metrics. The Mean Absolute Percentage Error - MAPE, measures the difference of forecast errors and divides it by the actual observation value. When MASE > 1, that means the model needs a lot of improvement. ![]() An MASE = 0.5, means that our model has doubled the prediction accuracy. When he have a MASE = 1, that means the model is exactly as good as just picking the last observation. We have also the Mean Absolute Scaled Error - MASE that measure the forecast error compared to the error of a naive forecast. MAE is the mean of all differences between actual and forecaset absolute value and in order to avoid negative values we can use RMSE. The simplest way to m ake a comparison is via scale dependent error because all the models need to be on the same scale using the Mean Absolute Error - MAE and the Root Mean Squared Error - RMSE. We start about how to compare different time seris models against each other.įorecast Accuracy It determine how much difference thare is between the actual value and the forecast for the value. ![]() In this post we will review the statistical background for time series analysis and forecasting. ![]()
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