  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.
