MLP-Mixer based Masked Autoencoders Are Effective, Explainable and Robust for Time Series Anomaly Detection

Summary: MMA: MLP-Mixer backbone + masked autoencoder enabling 10–20× larger input windows to detect long-duration subsequence anomalies, with contrastive learning to catch subtle anomalies. Dynamic anomaly filtering reduces false positives; robust to training contamination and provides explainable normal-pattern reconstructions. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 14237
Venue: VLDB
Year: 2025
Pagerank: 4.1945683e-05
Overall Rank: 10,876 | 24.34%
DOI: 10.14778/3712221.3712243

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Rank	Citing Paper	Year	Venue	Pagerank

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Showing 9 of 9 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
1,253	Anomaly Detection in Time Series: A Comprehensive Evaluation	2022	VLDB	0.00013032074
1,634	Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series	2021	VLDB	0.00011058945
2,029	SAND: Streaming Subsequence Anomaly Detection	2021	VLDB	9.740868e-05
2,290	TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data	2022	VLDB	9.0934125e-05
2,381	TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection	2022	VLDB	8.9327638e-05
2,644	Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series	2020	VLDB	8.3832357e-05
3,943	Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection	2022	VLDB	6.6099833e-05
4,079	Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series	2023	VLDB	6.4663636e-05
5,777	ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection	2024	VLDB	5.3308813e-05

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