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MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast

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arxiv 2410.15997 v1 pith:U7UVQTEN submitted 2024-10-21 cs.LG

Shiyan Hu , Kai Zhao , Xiangfei Qiu , Yang Shu , Jilin Hu , Bin Yang , Chenjuan Guo This is my paper
classification cs.LG
keywords anomalydetectionmultircpredictiontimelearninganomaliesdiverse
verification ladder T0 review T1 audit T2 compute T3 formal
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Many methods have been proposed for unsupervised time series anomaly detection. Despite some progress, research on predicting future anomalies is still relatively scarce. Predicting anomalies is particularly challenging due to the diverse reaction time and the lack of labeled data. To address these challenges, we propose MultiRC to integrate reconstructive and contrastive learning for joint learning of anomaly prediction and detection, with multi-scale structure and adaptive dominant period mask to deal with the diverse reaction time. MultiRC also generates negative samples to provide essential training momentum for the anomaly prediction tasks and prevent model degradation. We evaluate seven benchmark datasets from different fields. For both anomaly prediction and detection tasks, MultiRC outperforms existing state-of-the-art methods.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Enhancing Irregular Time Series Forecasting with Continuous-Time Modeling Framework

    cs.LG 2026-07 conditional novelty 5.0 of 10

    WrapFlow combines continuous-time event/gap tokenization with simulation-free residual flow matching on a Transformer to improve irregular multivariate time-series forecasting.

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    FADE combines adversarial training with trajectory preservation to erase concepts from diffusion models, reporting state-of-the-art erasure on Stable Diffusion benchmarks, but the evidence is incomplete and the theore...

  3. Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services

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    A Transformer plus multiscale attention-weighted fusion is claimed to improve cloud anomaly detection metrics by 2-3 points, but the missing label definition and artifacts block verification.

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