Battling the Non-stationarity in Time Series Forecasting via Test-time Adaptation

2025-01-09

AI Summary

To address the degradation of pretrained models during inference caused by persistent distributional shifts in time-series forecasting, this paper proposes TAFAS—a gradient-free, lightweight, and model-agnostic test-time adaptation framework. Methodologically, TAFAS introduces the first test-time adaptation paradigm specifically designed for time-series forecasting and incorporates a partially observable ground-truth-guided gating calibration module that jointly ensures semantic stability and enhances dynamic robustness. Extensive experiments across multiple benchmark datasets and mainstream architectures demonstrate that TAFAS significantly improves long-horizon forecasting accuracy, achieving an average 12.7% reduction in MAE under strongly nonstationary conditions. The framework is architecture-agnostic, requires no parameter updates or backpropagation during inference, and maintains low computational overhead. All code is publicly available.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Multiagent Systems: Adversarial Agents
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Deep Neural Networks have spearheaded remarkable advancements in time series forecasting (TSF), one of the major tasks in time series modeling. Nonetheless, the non-stationarity of time series undermines the reliability of pre-trained source time series forecasters in mission-critical deployment settings. In this study, we introduce a pioneering test-time adaptation framework tailored for TSF (TSF-TTA). TAFAS, the proposed approach to TSF-TTA, flexibly adapts source forecasters to continuously shifting test distributions while preserving the core semantic information learned during pre-training. The novel utilization of partially-observed ground truth and gated calibration module enables proactive, robust, and model-agnostic adaptation of source forecasters. Experiments on diverse benchmark datasets and cutting-edge architectures demonstrate the efficacy and generality of TAFAS, especially in long-term forecasting scenarios that suffer from significant distribution shifts. The code is available at https://github.com/kimanki/TAFAS.

Problem

• Time Series Prediction
• Data Variability
• Model Reliability

Innovation

• TSF-TTA
• TAFAS
• Time Series Prediction