Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting

TL;DR

ReIMTS is proposed, a Recursive multi-scale modeling approach for Irregular Multivariate Time Series forecasting that keeps timestamps unchanged and recursively splits each sample into subsamples with progressively shorter time periods to capture global-to-local dependencies for accurate forecasting.

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.

Figures (8)

• Figure 1: Existing multi-scale methods for IMTS resample the coarse series to balance differences in sampling densities across different variables. In the original sample from the healthcare dataset PhysioNet'12, liver function marker Bilirubin and heart rate (HR) exhibit a dense-to-sparse sampling pattern reflecting urgent to mild clinical monitoring, which is disrupted in the coarse series.
• Figure 2: The architecture of ReIMTS with three scale levels. For the original IMTS sample on the top left, ReIMTS recursively splits it into subsamples with shorter time periods at each scale level. ReIMTS is compatible with most IMTS models, and we use graph neural networks as backbones here to illustrate multi-scale variable representation learning. Local representations from lower scale levels are fused with global ones from upper scale levels. The decoder in the lowest scale concatenates representations and decodes them into forecast predictions.
• Figure 3: Comparison of our method and existing approaches. (a) ReIMTS preserves the original sampling pattern while remaining compatible with most IMTS backbones. (b) Sample-space resampling methods. (c) Patch-based methods for IMTS. (d) Representation-space resampling methods.
• Figure 4: Model efficiency comparison on MIMIC-III, with a 36-hour lookback length, 3 forecast timestamps, 96 variables, and a batch size of 32. ReIMTS uses GraFITi as backbone in the figure, which achieves the best efficiency compared to other multi-scale IMTS methods, including Warpformer, HD-TTS, and Hi-Patch.
• Figure 5: Effect of different time period lengths on PhysioNet'12, Human Activity, and USHCN.