Irregular multivariate time series (IMTS) is characterized by the lack of synchronized observations across its different channels. In this paper, we point out that this channel-wise asynchrony can lead to poor channel-wise modeling of existing deep learning methods. To overcome this limitation, we propose MTM, a multi-scale token mixing transformer for the classification of IMTS. We find that the channel-wise asynchrony can be alleviated by down-sampling the time series to coarser timescales, and propose to incorporate a masked concat pooling in MTM that gradually down-samples IMTS to enhance the channel-wise attention modules. Meanwhile, we propose a novel channel-wise token mixing mechanism which proactively chooses important tokens from one channel and mixes them with other channels, to further boost the channel-wise learning of our model. Through extensive experiments on real-world datasets and comparison with state-of-the-art methods, we demonstrate that MTM consistently achieves the best performance on all the benchmarks, with improvements of up to 3.8% in AUPRC for classification.
@article{arxiv.2509.17809,
title = {MTM: A Multi-Scale Token Mixing Transformer for Irregular Multivariate Time Series Classification},
author = {Shuhan Zhong and Weipeng Zhuo and Sizhe Song and Guanyao Li and Zhongyi Yu and S. -H. Gary Chan},
journal= {arXiv preprint arXiv:2509.17809},
year = {2025}
}