  As large-scale time-series data can easily be found in real-world
applications, multivariate time-series anomaly detection has played an
essential role in diverse industries. It enables productivity improvement and
maintenance cost reduction by preventing malfunctions and detecting anomalies
based on time-series data. However, multivariate time-series anomaly detection
is challenging because real-world time-series data exhibit complex temporal
dependencies. For this task, it is crucial to learn a rich representation that
effectively contains the nonlinear temporal dynamics of normal behavior. In
this study, we propose an unsupervised multivariate time-series anomaly
detection model named RAE-MEPC which learns informative normal representations
based on multi-resolution ensemble and predictive coding. We introduce
multi-resolution ensemble encoding to capture the multi-scale dependency from
the input time series. The encoder hierarchically aggregates the temporal
features extracted from the sub-encoders with different encoding lengths. From
these encoded features, the reconstruction decoder reconstructs the input time
series based on multi-resolution ensemble decoding where lower-resolution
information helps to decode sub-decoders with higher-resolution outputs.
Predictive coding is further introduced to encourage the model to learn the
temporal dependencies of the time series. Experiments on real-world benchmark
datasets show that the proposed model outperforms the benchmark models for
multivariate time-series anomaly detection.
