TsSHAP: Robust model agnostic feature-based explainability for time series forecasting

Understand

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.