Anomaly Detection For Time Series Data Based on Multi-granularity Neighbor Residual Network

Abstract

Anomaly detection of time series data is one of the most important problems in industrial applications. Most of methods are difficult to obtain rich features of samples due to the lack of dimension. In this paper, we develop an anomaly detection method for time series data based on multi-granularity neighbor residual network (MGNRN). First, we construct a neighbor input vector with a sliding time window for each data sample, and define neighbor-based input matrix by considering multi-granularity neighborhood features. Second, we compute the linear and non-linear neighbor features of multi-granularity time windows for the sample. Finally, by combining the linear neighborhood residual with nonlinear residual, we predict the abnormal probability of the sample. Experiments verify the multi-granularity neighbor residual network improves the accuracy of abnormal detection, and show good performance on precision and F1 metrics.