Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting
arXiv:2602.21498v1 Announce Type: new 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 depend
arXiv:2602.21498v1 Announce Type: new 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 27.1\% in the forecasting task across different models and real-world datasets. Our code is available at https://github.com/Ladbaby/PyOmniTS.
Executive Summary
The article proposes ReIMTS, a recursive multi-scale modeling approach for irregular multivariate time series forecasting. ReIMTS preserves the original timestamps and sampling pattern information by recursively splitting samples into subsamples with shorter time periods. This approach enables the capture of global-to-local dependencies, leading to improved forecasting accuracy. Extensive experiments demonstrate a significant performance improvement of 27.1% across different models and real-world datasets.
Key Points
- ▸ ReIMTS approach for irregular multivariate time series forecasting
- ▸ Preservation of original timestamps and sampling pattern information
- ▸ Recursive splitting of samples into subsamples with shorter time periods
Merits
Improved Forecasting Accuracy
ReIMTS demonstrates a significant performance improvement of 27.1% in forecasting tasks across different models and real-world datasets.
Demerits
Computational Complexity
The recursive splitting of samples into subsamples may increase computational complexity, potentially limiting the approach's scalability.
Expert Commentary
The proposed ReIMTS approach addresses a significant challenge in irregular multivariate time series forecasting by preserving the original timestamps and sampling pattern information. The recursive splitting of samples into subsamples enables the capture of complex dependencies across multiple time scales, leading to improved forecasting accuracy. However, further research is needed to address potential limitations, such as computational complexity, and to explore the applicability of ReIMTS to various domains.
Recommendations
- ✓ Further evaluation of ReIMTS on diverse datasets to assess its robustness and generalizability
- ✓ Investigation of techniques to reduce computational complexity and improve scalability
