Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data
This addresses the challenge of detecting anomalies in complex multi-variate time series data, which is crucial for applications like industrial monitoring or healthcare, but it appears incremental as it builds on existing contrastive predictive coding techniques.
The paper tackles anomaly detection in multi-variate time series data by proposing TRL-CPC, a method that learns representations through contrastive predictive coding to model temporal dependencies and correlations, achieving superior performance against state-of-the-art methods on three datasets.
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.