Autorelevance function and other feature relevance measures for univariate time series
We propose a model agnostic methodology to measure lag relevance in machine learning forecasting models applied to univariate time series. Particularly, we are working in the context of time series using the frameworks of Ghost variables and Shapley values, together with additive importance measures, to introduce the auto-relevance and partial auto-relevance functions as the lag importance values. Additionally, we propose a novel method to replace absent features in coalition based methods with a one step forecast from the same model. We evaluate these proposals under different simulations and real data cases. This combined framework perspective is particularly suitable for time series. I...
| Authors | Julian Cardenas, Jamie Arjona, Pedro Delicado |
|---|---|
| Primary category | stat.ML |
| Published | 2 July 2026 |
| Last updated at source | 2 July 2026 |
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