  Time series forecasting is a demanding task ranging from weather to failure
forecasting with black-box models achieving state-of-the-art performances.
However, understanding and debugging are not guaranteed. We propose TS-MULE, a
local surrogate model explanation method specialized for time series extending
the LIME approach. Our extended LIME works with various ways to segment and
perturb the time series data. In our extension, we present six sampling
segmentation approaches for time series to improve the quality of surrogate
attributions and demonstrate their performances on three deep learning model
architectures and three common multivariate time series datasets.
