# MASE

## Contents

The following are my learnings from the Hyndman-MASE.

- Scale dependent errors
- Mean Absolute Error
- Geometric Mean Absolute Error
- Mean Square Error

- Percentage Error
- MAPE
- sMAPE - to avoid the problem with MAPE where it puts more penalty on positive errors as compared to negative errors

- Relative error : Divide each error by the error obtained by using some benchmark method
- Scale-free method: Scale the error by mean absolute naive forecast error. Mean of all scaled errors
- MASE is suitable even when the data exhibit a trend or seasonal pattern
- Typical values for one-step forecasts are less than 1
- Multi-Step MASE are often larger than one as it becomes difficult to forecast as horizon increases
- MASE works for all types of forecasting scenarios