Automatic Adaptive Conformal Inference
Makhin, Artem Panov, Maxim
Makhin, Artem
Panov, Maxim
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Department
Machine Learning
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Conference proceeding
Date
2025
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English
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Abstract
This paper proposes a new method for conformal inference in time series forecasting that works under any changes in the data generation process. It can be used with any black box forecasting models that predict the future values of a time series. Unlike existing methods, the proposed method adapts to changes in the distribution faster and in a more predictable way. We achieve this by modifying the adaptive conformal inference (ACI) algorithm of Gibbs and Candès (2021) by replacing the constant learning rate parameter with the one that adapts to changes in the data generation process. We tested our method on real datasets and showed the absence of sharp explosions in the width of intervals common for other adaptive conformal prediction approaches.
Citation
A. Makhin and M. Panov, “Automatic Adaptive Conformal Inference for Time Series Forecasting,” pp. 262–274, 2025, doi: 10.1007/978-3-031-88036-0_16.
Source
Analysis of Images, Social Networks and Texts: 12th International Conference, AIST 2024
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Springer Nature