Revitalizing Canonical Pre-Alignment for Irregular Multivariate Time Series Forecasting

• URL: http://arxiv.org/abs/2508.01971v1
• Date: Mon, 04 Aug 2025 01:07:24 GMT
• Title: Revitalizing Canonical Pre-Alignment for Irregular Multivariate Time Series Forecasting
• Authors: Ziyu Zhou, Yiming Huang, Yanyun Wang, Yuankai Wu, James Kwok, Yuxuan Liang,
• Abstract summary: We propose KAFNet, a compact architecture grounded in Canonical Pre-Alignment (CPA) for IMTS forecasting.<n>KAFNet achieves state-of-the-art forecasting performance, with a 7.2$times$ parameter reduction and a 8.4$times$ training-inference acceleration.
• Score: 17.046106977768215
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Irregular multivariate time series (IMTS), characterized by uneven sampling and inter-variate asynchrony, fuel many forecasting applications yet remain challenging to model efficiently. Canonical Pre-Alignment (CPA) has been widely adopted in IMTS modeling by padding zeros at every global timestamp, thereby alleviating inter-variate asynchrony and unifying the series length, but its dense zero-padding inflates the pre-aligned series length, especially when numerous variates are present, causing prohibitive compute overhead. Recent graph-based models with patching strategies sidestep CPA, but their local message passing struggles to capture global inter-variate correlations. Therefore, we posit that CPA should be retained, with the pre-aligned series properly handled by the model, enabling it to outperform state-of-the-art graph-based baselines that sidestep CPA. Technically, we propose KAFNet, a compact architecture grounded in CPA for IMTS forecasting that couples (1) Pre-Convolution module for sequence smoothing and sparsity mitigation, (2) Temporal Kernel Aggregation module for learnable compression and modeling of intra-series irregularity, and (3) Frequency Linear Attention blocks for the low-cost inter-series correlations modeling in the frequency domain. Experiments on multiple IMTS datasets show that KAFNet achieves state-of-the-art forecasting performance, with a 7.2$\times$ parameter reduction and a 8.4$\times$ training-inference acceleration.

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