  Nowadays, multivariate time series data are increasingly collected in various
real world systems, e.g., power plants, wearable devices, etc. Anomaly
detection and diagnosis in multivariate time series refer to identifying
abnormal status in certain time steps and pinpointing the root causes. Building
such a system, however, is challenging since it not only requires to capture
the temporal dependency in each time series, but also need encode the
inter-correlations between different pairs of time series. In addition, the
system should be robust to noise and provide operators with different levels of
anomaly scores based upon the severity of different incidents. Despite the fact
that a number of unsupervised anomaly detection algorithms have been developed,
few of them can jointly address these challenges. In this paper, we propose a
Multi-Scale Convolutional Recurrent Encoder-Decoder (MSCRED), to perform
anomaly detection and diagnosis in multivariate time series data. Specifically,
MSCRED first constructs multi-scale (resolution) signature matrices to
characterize multiple levels of the system statuses in different time steps.
Subsequently, given the signature matrices, a convolutional encoder is employed
to encode the inter-sensor (time series) correlations and an attention based
Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture
the temporal patterns. Finally, based upon the feature maps which encode the
inter-sensor correlations and temporal information, a convolutional decoder is
used to reconstruct the input signature matrices and the residual signature
matrices are further utilized to detect and diagnose anomalies. Extensive
empirical studies based on a synthetic dataset and a real power plant dataset
demonstrate that MSCRED can outperform state-of-the-art baseline methods.
