mlsanityData to 14 Oct 2026

Surrogate Modeling for Explainable Predictive Time Series Corrections

arXiv 2412.19897 ↗First posted 27 Dec 2024stat.ML cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 20 of its 67 references that are in the map. First posted on arXiv 27 Dec 2024.

Builds on

20 of 67 references.

References · 20

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8 more
  1. 2020OptiLIME: Optimized LIME Explanations for Diagnostic Computer Algorithmscited by 30
  2. 2021TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Modelscited by 28
  3. 2022WindowSHAP: An Efficient Framework for Explaining Time-series Classifiers based on Shapley Valuescited by 24
  4. 2024KernelSHAP-IQ: Weighted Least-Square Optimization for Shapley Interactionscited by 22
  5. 2021Towards a Rigorous Evaluation of Explainability for Multivariate Time Seriescited by 10
  6. 2024Toward Physics-guided Time Series Embeddingcited by 3
  7. 2021Explainable AI by BAPC -- Before and After correction Parameter Comparisoncited by 1
  8. 2024Using a Local Surrogate Model to Interpret Temporal Shifts in Global Annual Datacited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026When Do Corrective Features Help? An Agent for Corrective Feature Discovery on Black-Box Forecasterscited by 0