  This paper proposes LPC-AD, a fast and accurate multivariate time series
(MTS) anomaly detection method. LPC-AD is motivated by the ever-increasing
needs for fast and accurate MTS anomaly detection methods to support fast
troubleshooting in cloud computing, micro-service systems, etc. LPC-AD is fast
in the sense that its reduces the training time by as high as 38.2% compared to
the 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 as
high as 18.9% compared to SOTA sophisticated deep learning methods that focus
on enhancing detection accuracy. Methodologically, LPC-AD contributes a generic
architecture LPC-Reconstruct for one to attain different trade-offs between
training speed and detection accuracy. More specifically, LPC-Reconstruct is
built on ideas from autoencoder for reducing redundancy in time series, latent
predictive coding for capturing temporal dependence in MTS, and randomized
perturbation for avoiding overfitting of anomalous dependence in the training
data. We present simple instantiations of LPC-Reconstruct to attain fast
training speed, where we propose a simple randomized perturbation method. The
superior performance of LPC-AD over SOTA methods is validated by extensive
experiments on four large real-world datasets. Experiment results also show the
necessity and benefit of each component of the LPC-Reconstruct architecture and
that LPC-AD is robust to hyper parameters.
