Surrogate Modeling for Explainable Predictive Time Series Corrections
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866916567507271680 |
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| author | Lopez, Alfredo Sobieczky, Florian |
| author_facet | Lopez, Alfredo Sobieczky, Florian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_19897 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Surrogate Modeling for Explainable Predictive Time Series Corrections Lopez, Alfredo Sobieczky, Florian Machine Learning 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. |
| title | Surrogate Modeling for Explainable Predictive Time Series Corrections |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2412.19897 |