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

• URL: http://arxiv.org/abs/2602.00582v1
• Date: Sat, 31 Jan 2026 07:49:44 GMT
• Title: Bridging Time and Frequency: A Joint Modeling Framework for Irregular Multivariate Time Series Forecasting
• Authors: Xiangfei Qiu, Kangjia Yan, Xvyuan Liu, Xingjian Wu, Jilin Hu,
• Abstract summary: We propose TFMixer, a joint time-frequency modeling framework for IMTS forecasting.<n>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.<n>In parallel, the Local Time Module introduces a query-based patch mixing mechanism to adaptively aggregate informative temporal patches and alleviate information density imbalance.
• Score: 7.6757168009144126
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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