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A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis
Summary: MSD-Mixer: an MLP‑Mixer that explicitly decomposes uni-/multivariate time series using multi-scale temporal patching and MLPs to model intra/inter-patch dynamics and channel correlations. New loss constrains residual mean and autocorrelation for decomposition completeness; achieves consistent, efficient SOTA across five time-series tasks.
(summarized by gpt-5-mini on Feb 09 2026)
Paper ID
13599
Venue
VLDB
Year
2024
Pagerank
5.3483178e-05
Overall Rank
8,915 | 38.84%
DOI
10.14778/3654621.3654637
Incoming Non-self Citations Over Time
Authors
1.
Shuhan Zhong
(Hong Kong University of Science and Technology)
2.
Sizhe Song
(Hong Kong University of Science and Technology)
3.
Weipeng Zhuo
(Beijing Institute of Technology)
4.
Guanyao Li
(Guangdong University of Technology)
5.
Yang Liu
(Guangdong University of Technology)
6.
S.-H. Gary Chan
(Hong Kong University of Science and Technology)
BibTeX Citation
Copy BibTeX
@article{zhong_vldb24,
title = {{A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis}},
author = {Zhong, Shuhan and Song, Sizhe and Zhuo, Weipeng and Li, Guanyao and Liu, Yang and Chan, S.-H. Gary},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {7},
pages = {1723--1736},
doi = {10.14778/3654621.3654637},
url = {https://doi.org/10.14778/3654621.3654637},
year = {2024}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
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