A Comparative Analysis of Image Encoding of Time Series for Anomaly Detection

Abstract

A novel approach to anomaly detection in time series data is based on the use of multivariate image analysis techniques. With this approach, time series are encoded as images that make them amenable to analysis by pretrained deep neural networks. Few studies have evaluated the merits of the different image encoding algorithms, and in this investigation, encoding of time series data with Euclidean distance plots or unthresholded recurrence plots, Gramian angular fields, Morlet wavelet scalograms, and an ad hoc approach based on the presentation of the raw time series data in a stacked format are compared. This is done based on three case studies where features are extracted from the images with gray level co-occurrence matrices, local binary patterns and the use of a pretrained convolutional neural network, GoogleNet. Although no method consistently outperformed all the other methods, the Euclidean distance plots and GoogleNet features yielded the best results.