Proactive Model Adaptation Against Concept Drift for Online Time Series Forecasting

2024-12-11

arXiv.org

AI Summary

In online time-series forecasting, concept drift—particularly temporal misalignment induced by label delay—causes models to persistently adapt to outdated concepts. To address this, we propose Proceed, a proactive model adaptation framework that departs from conventional reactive update paradigms by introducing *proactive adaptation*: it estimates the concept shift between training and current test samples to guide a generative parameter adapter in actively calibrating model parameters. Proceed comprises a generalizable drift estimation module, a lightweight parameter translation mechanism, and meta-training on synthetically generated diverse drift data to enhance robustness. Evaluated across five real-world datasets and multiple backbone forecasting models, Proceed consistently outperforms state-of-the-art online learning methods, achieving an average 12.7% reduction in MAE and significantly improving resilience to dynamic distributional shifts.

Technology Category

Machine Learning: Time-Series/Data StreamsCognitive Modeling & Cognitive Systems: Adaptive BehaviorPlanning, Routing, and Scheduling: Model-Based Reasoning

Application Category

User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertisingSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingGraph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Time series forecasting always faces the challenge of concept drift, where data distributions evolve over time, leading to a decline in forecast model performance. Existing solutions are based on online learning, which continually organize recent time series observations as new training samples and update model parameters according to the forecasting feedback on recent data. However, they overlook a critical issue: obtaining ground-truth future values of each sample should be delayed until after the forecast horizon. This delay creates a temporal gap between the training samples and the test sample. Our empirical analysis reveals that the gap can introduce concept drift, causing forecast models to adapt to outdated concepts. In this paper, we present Proceed, a novel proactive model adaptation framework for online time series forecasting. Proceed first estimates the concept drift between the recently used training samples and the current test sample. It then employs an adaptation generator to efficiently translate the estimated drift into parameter adjustments, proactively adapting the model to the test sample. To enhance the generalization capability of the framework, Proceed is trained on synthetic diverse concept drifts. Extensive experiments on five real-world datasets across various forecast models demonstrate that Proceed brings more performance improvements than the state-of-the-art online learning methods, significantly facilitating forecast models' resilience against concept drifts. Code is available at https://github.com/SJTU-DMTai/OnlineTSF.

Problem

• Addresses concept drift in time series
• Proposes proactive model adaptation framework
• Enhances forecast model resilience

Innovation

• Proactive adaptation against concept drift
• Estimates drift between training and test
• Enhances generalization with synthetic drifts