Performance metrics for multi-step forecasting measuring win-loss, seasonal variance and forecast stability: an empirical study
Eivind Strøm,
Odd Erik Gundersen
Abstract:This paper addresses the evaluation of multi-step point forecasting models. Currently, deep learning models for multi-step forecasting are evaluated on datasets by selecting one error metric that is aggregated across the time series and the forecast horizon. This approach hides insights that would otherwise be useful for practitioners when evaluating and selecting forecasting models. We propose four novel metrics to provide additional insights when evaluating models: 1) a win-loss metric that shows how models … Show more
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