Is there a substitute for shap.KernelExplainer for time series forecasting?

I am working on a series of forecasting models with high dimensional input, so varying each time lag of each feature is both too computationally expensive to be feasible and unlikely to give reasonable results. Is there a way to provide user-defined "superpixel"-esque time segments to mask for KernelExplainer or a similar tool?

I'm probably going to have write my own code for eventually but it would be very good if I could get an approximate solution put together quickly. …

datascience forecasting shap time time series time series forecasting