![[2509.03810] Online time series prediction using feature adjustment](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2509.03810] Online time series prediction using feature adjustment
Summary
The paper presents a novel approach to online time series prediction, addressing challenges related to distribution shifts and delayed feedback through a method called ADAPT-Z, which enhances feature representation updates.
Why It Matters
This research is significant as it tackles the critical issue of adapting time series models to changing data distributions in real-time applications. By improving prediction accuracy in online settings, it has implications for various industries reliant on timely data analysis, such as finance and healthcare.
Key Takeaways
- Current online time series methods focus on parameter selection and update strategies.
- ADAPT-Z introduces a new way to update feature representations to handle distribution shifts effectively.
- The method shows superior performance compared to standard models and state-of-the-art approaches across multiple datasets.
Computer Science > Machine Learning arXiv:2509.03810 (cs) [Submitted on 4 Sep 2025 (v1), last revised 26 Feb 2026 (this version, v2)] Title:Online time series prediction using feature adjustment Authors:Xiannan Huang, Shuhan Qiu, Jiayuan Du, Chao Yang View a PDF of the paper titled Online time series prediction using feature adjustment, by Xiannan Huang and 3 other authors View PDF HTML (experimental) 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 ad...
