Surrogate Modeling for Explainable Predictive Time Series Corrections

ABSTRACT

We introduce a local surrogate approach for explainable time‐series analysis. An initially non‐interpretable predictive model to improve the forecast of a classical time‐series “base model” is used. “Explainability” of the correction is provided by fitting the base model again to the data from which the error prediction is removed (subtracted), yielding a difference in the model parameters which can be interpreted. We provide illustrative examples to demonstrate the potential of the method to discover and explain anomalies by new types of plots showing and comparing the features' importance as a function of time and relative to each other.