  A trustworthy machine learning model should be accurate as well as
explainable. Understanding why a model makes a certain decision defines the
notion of explainability. While various flavors of explainability have been
well-studied in supervised learning paradigms like classification and
regression, literature on explainability for time series forecasting is
relatively scarce.
  In this paper, we propose a feature-based explainability algorithm, TsSHAP,
that can explain the forecast of any black-box forecasting model. The method is
agnostic of the forecasting model and can provide explanations for a forecast
in terms of interpretable features defined by the user a prior.
  The explanations are in terms of the SHAP values obtained by applying the
TreeSHAP algorithm on a surrogate model that learns a mapping between the
interpretable feature space and the forecast of the black-box model.
  Moreover, we formalize the notion of local, semi-local, and global
explanations in the context of time series forecasting, which can be useful in
several scenarios. We validate the efficacy and robustness of TsSHAP through
extensive experiments on multiple datasets.
