🤖 AI Summary
To address degraded model adaptability in online multistep time-series forecasting—caused by data distribution drift and delayed ground-truth feedback—this paper proposes ADAPT-Z. Methodologically, ADAPT-Z abandons conventional parameter fine-tuning and instead models the dynamics of latent factors. It introduces an adapter module that fuses current features with historical gradient information within a learned Z-space, enabling persistent tracking and incremental self-adaptation of feature representations. This design mitigates gradient mismatch induced by label delay and enhances robustness to non-stationary data. Empirical evaluation across multiple benchmark datasets demonstrates that ADAPT-Z significantly outperforms static baselines and state-of-the-art online learning methods, achieving superior generalization and sustained adaptive capability under streaming conditions.
📝 Abstract
Time series forecasting is of significant importance across various domains. However, it faces significant challenges due to distribution shift. This issue becomes particularly pronounced in online deployment scenarios where data arrives sequentially, requiring models to adapt continually to evolving patterns. Current time series online learning methods focus on two main aspects: selecting suitable parameters to update (e.g., final layer weights or adapter modules) and devising suitable update strategies (e.g., using recent batches, replay buffers, or averaged gradients). We challenge the conventional parameter selection approach, proposing that distribution shifts stem from changes in underlying latent factors influencing the data. Consequently, updating the feature representations of these latent factors may be more effective. To address the critical problem of delayed feedback in multi-step forecasting (where true values arrive much later than predictions), we introduce ADAPT-Z (Automatic Delta Adjustment via Persistent Tracking in Z-space). ADAPT-Z utilizes an adapter module that leverages current feature representations combined with historical gradient information to enable robust parameter updates despite the delay. Extensive experiments demonstrate that our method consistently outperforms standard base models without adaptation and surpasses state-of-the-art online learning approaches across multiple datasets. The code is available at https://github.com/xiannanhuang/ADAPT-Z.