Anomaly Detection in Time Series Data Based on Unthresholded Recurrence Plots

The problem of detecting anomalies in time series has attracted much attention recently due to its numerous applications. In this paper, we proposed a novel framework based on nonlinear methods. Firstly, we use time-dependent URP (Unthreholded Recurrence Plots) to represent time series, so as to capture the nonlinear characteristic of time series. Secondly, we present ELM-AE (Extreme Learning Machine Auto-Encoder) based algorithm to learn the main features of URPs. Finally, we compute residual errors as anomaly score for each time series points. This framework can perform well on complex nonlinear systems using unlabeled datasets.