Another Look at Forecast Accuracy Metrics for Intermittent Demand

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Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology (2018, methodology): “…of employing performance metrics to measure how much forecasts deviate from observations in order to assess quality and choose forecasting methods, especially in support of supply chain or predicting workload for software development (e.g. Carbone and Armstrong, 1982; De Gooijer and Hyndman, 2006).”
Combination of long term and short term forecasts, with application to tourism demand forecasting (2011, background): “The second error measure is the mean absolute scaled error (MASE), proposed by Hyndman (2006) and Hyndman and Koehler (2006).”