Surrogate Modeling for Explainable Predictive Time Series Corrections

Alfredo Lopez, Florian Sobieczky
12/27/2024
stat.MLcs.LG

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Abstract

We introduce a local surrogate approach for explainable time-series forecasting. An initially non-interpretable predictive model to improve the forecast of a classical time-series 'base model' is used. 'Explainability' of the correction is provided by fitting the base model again to the data from which the error prediction is removed (subtracted), yielding a difference in the model parameters which can be interpreted. We provide illustrative examples to demonstrate the potential of the method to discover and explain underlying patterns in the data.

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Cite this paper

@article{lopez2024surrogate,
  title  = {Surrogate Modeling for Explainable Predictive Time Series Corrections},
  author = {Alfredo Lopez and Florian Sobieczky},
  year   = {2024},
  eprint = {2412.19897},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.19897v2}
}

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