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

2026-02-24

AI Summary

This work addresses the challenge of forecasting irregularly sampled multivariate time series (IMTS), where existing approaches often rely on resampling that disrupts the intrinsic sampling patterns encoded in the original timestamps, thereby degrading predictive performance. To overcome this limitation, the authors propose ReIMTS, a novel framework that eschews resampling entirely. Instead, it recursively partitions the input into variable-length subsequences to preserve the native temporal structure and introduces an irregularity-aware representation fusion mechanism that jointly captures multiscale temporal dependencies—from global to local—and inter-variable relationships. Extensive experiments on multiple real-world datasets demonstrate that ReIMTS achieves an average improvement of 27.1% in forecasting accuracy, significantly outperforming current state-of-the-art methods.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Irregular Multivariate Time Series
• multi-scale dependencies
• sampling pattern
• time series forecasting
• irregular timestamps

Innovation

• Irregular Multivariate Time Series
• Recursive Multi-Scale Modeling
• Timestamp-Preserving
• Representation Fusion
• Time Series Forecasting