  Global Forecasting Models (GFM) that are trained across a set of multiple
time series have shown superior results in many forecasting competitions and
real-world applications compared with univariate forecasting approaches. One
aspect of the popularity of statistical forecasting models such as ETS and
ARIMA is their relative simplicity and interpretability (in terms of relevant
lags, trend, seasonality, and others), while GFMs typically lack
interpretability, especially towards particular time series. This reduces the
trust and confidence of the stakeholders when making decisions based on the
forecasts without being able to understand the predictions. To mitigate this
problem, in this work, we propose a novel local model-agnostic interpretability
approach to explain the forecasts from GFMs. We train simpler univariate
surrogate models that are considered interpretable (e.g., ETS) on the
predictions of the GFM on samples within a neighbourhood that we obtain through
bootstrapping or straightforwardly as the one-step-ahead global black-box model
forecasts of the time series which needs to be explained. After, we evaluate
the explanations for the forecasts of the global models in both qualitative and
quantitative aspects such as accuracy, fidelity, stability and
comprehensibility, and are able to show the benefits of our approach.
