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TimeMixer

TimeMixer is a decomposition-based, attention-free time-series forecasting architecture introduced by Wang et al. at ICLR 2024. The central idea is to disentangle seasonal and trend components across multiple temporal scales constructed by average pooling, then mix information across those scales using lightweight MLP blocks. By handling coarse (trend-dominant) and fine (seasonal-dominant) resolutions separately and combining their predictions, TimeMixer avoids the quadratic cost of attention while capturing both local and global temporal patterns.

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TimeMixer (Decomposable Multiscale Mixing)

Taxonomický záznam metódy · ml-model / deep-learning

Wang, S., Wu, H., Shi, X., Hu, T., Luo, H., Ma, L., Zhang, J. Y., & Zhou, J. (2024). TimeMixer: Decomposable multiscale mixing for time series forecasting. ICLR. · URL

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Taxonomic bucketDLinearmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketTimesNetmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketTSMixermachine-suggested · Relational suggestion, not evidence.