Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift

• URL: http://arxiv.org/abs/2310.14838v2
• Date: Tue, 11 Jun 2024 14:07:17 GMT
• Title: Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift
• Authors: Mouxiang Chen, Lefei Shen, Han Fu, Zhuo Li, Jianling Sun, Chenghao Liu,
• Abstract summary: We introduce a universal calibration methodology for the detection and adaptation of context-driven distribution shifts. A novel CDS detector, termed the "residual-based CDS detector" or "Reconditionor", quantifies the model's vulnerability to CDS. A high Reconditionor score indicates a severe susceptibility, thereby necessitating model adaptation.
• Score: 28.73747033245012
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent years have witnessed the success of introducing deep learning models to time series forecasting. From a data generation perspective, we illustrate that existing models are susceptible to distribution shifts driven by temporal contexts, whether observed or unobserved. Such context-driven distribution shift (CDS) introduces biases in predictions within specific contexts and poses challenges for conventional training paradigms. In this paper, we introduce a universal calibration methodology for the detection and adaptation of CDS with a trained model. To this end, we propose a novel CDS detector, termed the "residual-based CDS detector" or "Reconditionor", which quantifies the model's vulnerability to CDS by evaluating the mutual information between prediction residuals and their corresponding contexts. A high Reconditionor score indicates a severe susceptibility, thereby necessitating model adaptation. In this circumstance, we put forth a straightforward yet potent adapter framework for model calibration, termed the "sample-level contextualized adapter" or "SOLID". This framework involves the curation of a contextually similar dataset to the provided test sample and the subsequent fine-tuning of the model's prediction layer with a limited number of steps. Our theoretical analysis demonstrates that this adaptation strategy can achieve an optimal bias-variance trade-off. Notably, our proposed Reconditionor and SOLID are model-agnostic and readily adaptable to a wide range of models. Extensive experiments show that SOLID consistently enhances the performance of current forecasting models on real-world datasets, especially on cases with substantial CDS detected by the proposed Reconditionor, thus validating the effectiveness of the calibration approach.

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