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Reliability estimation of Machine Learning (ML) models is becoming a crucialsubject. This is particularly the case when such \mbox{models} are deployed insafety-critical applications, as the decisions based on model predictions canresult in hazardous situations. In this regard, recent research has proposedmethods to achieve safe, \mbox{dependable}, and reliable ML systems. One suchmethod consists of detecting and analyzing distributional shift, and thenmeasuring how such systems respond to these shifts. This was proposed inearlier work in SafeML. This work focuses on the use of SafeML for time seriesdata, and on reliability and robustness estimation of ML-forecasting methodsusing statistical distance measures. To this end, distance measures based onthe Empirical Cumulative Distribution Function (ECDF) proposed in SafeML areexplored to measure Statistical-Distance Dissimilarity (SDD) across timeseries. We then propose SDD-based Reliability Estimate (StaDRe) and SDD-basedRobustness (StaDRo) measures. With the help of a clustering technique, thesimilarity between the statistical properties of data seen during training andthe forecasts is identified. The proposed method is capable of providing a linkbetween dataset SDD and Key Performance Indicators (KPIs) of the ML models.

StaDRe and StaDRo: Reliability and Robustness Estimation of ML-based Forecasting using Statistical Distance Measures