Bridging Time and Frequency: A Joint Modeling Framework for Irregular Multivariate Time Series Forecasting

2026-01-31

AI Summary

This work addresses the challenges of capturing local dynamics and modeling global periodic structures in irregular multivariate time series, which arise from non-uniform sampling and asynchronous variables. To this end, we propose TFMixer, a novel framework that enables end-to-end joint time–frequency modeling for the first time. TFMixer introduces a learnable non-uniform discrete Fourier transform (NUDFT) to directly handle irregular sampling, integrates a query-driven temporal patch mixing mechanism to adaptively aggregate local information, and explicitly extrapolates seasonal components via an inverse NUDFT. The final prediction is derived by fusing representations from both time and frequency domains. Extensive experiments on multiple real-world datasets demonstrate that TFMixer significantly outperforms existing methods, achieving state-of-the-art performance.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Computer Vision: Multi-modal Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Irregular multivariate time series forecasting (IMTSF) is challenging due to non-uniform sampling and variable asynchronicity. These irregularities violate the equidistant assumptions of standard models, hindering local temporal modeling and rendering classical frequency-domain methods ineffective for capturing global periodic structures. To address this challenge, we propose TFMixer, a joint time-frequency modeling framework for IMTS forecasting. Specifically, TFMixer incorporates a Global Frequency Module that employs a learnable Non-Uniform Discrete Fourier Transform (NUDFT) to directly extract spectral representations from irregular timestamps. In parallel, the Local Time Module introduces a query-based patch mixing mechanism to adaptively aggregate informative temporal patches and alleviate information density imbalance. Finally, TFMixer fuses the time-domain and frequency-domain representations to generate forecasts and further leverages inverse NUDFT for explicit seasonal extrapolation. Extensive experiments on real-world datasets demonstrate the state--of-the-art performance of TFMixer.

Problem

• Irregular multivariate time series forecasting
• non-uniform sampling
• variable asynchronicity
• time-frequency modeling
• periodic structures

Innovation

• Non-Uniform Discrete Fourier Transform
• Joint Time-Frequency Modeling
• Irregular Multivariate Time Series
• Patch Mixing
• Seasonal Extrapolation