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This paper proposes LPC-AD, a fast and accurate multivariate time series(MTS) anomaly detection method. LPC-AD is motivated by the ever-increasingneeds for fast and accurate MTS anomaly detection methods to support fasttroubleshooting in cloud computing, micro-service systems, etc. LPC-AD is fastin the sense that its reduces the training time by as high as 38.2% compared tothe state-of-the-art (SOTA) deep learning methods that focus on training speed.LPC-AD is accurate in the sense that it improves the detection accuracy by ashigh as 18.9% compared to SOTA sophisticated deep learning methods that focuson enhancing detection accuracy. Methodologically, LPC-AD contributes a genericarchitecture LPC-Reconstruct for one to attain different trade-offs betweentraining speed and detection accuracy. More specifically, LPC-Reconstruct isbuilt on ideas from autoencoder for reducing redundancy in time series, latentpredictive coding for capturing temporal dependence in MTS, and randomizedperturbation for avoiding overfitting of anomalous dependence in the trainingdata. We present simple instantiations of LPC-Reconstruct to attain fasttraining speed, where we propose a simple randomized perturbation method. Thesuperior performance of LPC-AD over SOTA methods is validated by extensiveexperiments on four large real-world datasets. Experiment results also show thenecessity and benefit of each component of the LPC-Reconstruct architecture andthat LPC-AD is robust to hyper parameters.

LPC-AD: Fast and Accurate Multivariate Time Series Anomaly Detection via Latent Predictive Coding