![[2602.21498] Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.21498] Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting
Summary
The paper presents ReIMTS, a new approach for forecasting irregular multivariate time series by preserving original timestamps and capturing dependencies across multiple scales.
Why It Matters
This research addresses the limitations of existing methods that alter timestamps through resampling, which can lead to loss of valuable temporal information. By introducing a recursive modeling approach that maintains the integrity of the original data, it enhances forecasting accuracy, making it significant for fields relying on time series analysis, such as finance and healthcare.
Key Takeaways
- ReIMTS preserves original timestamps for better accuracy.
- The method captures global-to-local dependencies effectively.
- Experiments show an average performance improvement of 27.1%.
Computer Science > Machine Learning arXiv:2602.21498 (cs) [Submitted on 25 Feb 2026] Title:Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting Authors:Boyuan Li, Zhen Liu, Yicheng Luo, Qianli Ma View a PDF of the paper titled Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting, by Boyuan Li and 3 other authors View PDF HTML (experimental) Abstract:Irregular Multivariate Time Series (IMTS) are characterized by uneven intervals between consecutive timestamps, which carry sampling pattern information valuable and informative for learning temporal and variable dependencies. In addition, IMTS often exhibit diverse dependencies across multiple time scales. However, many existing multi-scale IMTS methods use resampling to obtain the coarse series, which can alter the original timestamps and disrupt the sampling pattern information. To address the challenge, we propose ReIMTS, a Recursive multi-scale modeling approach for Irregular Multivariate Time Series forecasting. Instead of resampling, ReIMTS keeps timestamps unchanged and recursively splits each sample into subsamples with progressively shorter time periods. Based on the original sampling timestamps in these long-to-short subsamples, an irregularity-aware representation fusion mechanism is proposed to capture global-to-local dependencies for accurate forecasting. Extensive experiments demonstrate an average performance improvement of...
