  Anomaly detection in multi-variate time series (MVTS) data is a huge
challenge as it requires simultaneous representation of long term temporal
dependencies and correlations across multiple variables. More often, this is
solved by breaking the complexity through modeling one dependency at a time. In
this paper, we propose a Time-series Representational Learning through
Contrastive Predictive Coding (TRL-CPC) towards anomaly detection in MVTS data.
First, we jointly optimize an encoder, an auto-regressor and a non-linear
transformation function to effectively learn the representations of the MVTS
data sets, for predicting future trends. It must be noted that the context
vectors are representative of the observation window in the MTVS. Next, the
latent representations for the succeeding instants obtained through non-linear
transformations of these context vectors, are contrasted with the latent
representations of the encoder for the multi-variables such that the density
for the positive pair is maximized. Thus, the TRL-CPC helps to model the
temporal dependencies and the correlations of the parameters for a healthy
signal pattern. Finally, fitting the latent representations are fit into a
Gaussian scoring function to detect anomalies. Evaluation of the proposed
TRL-CPC on three MVTS data sets against SOTA anomaly detection methods shows
the superiority of TRL-CPC.
