Research on Multi-dimensional Time Series Anomaly Detection Method Based on Temporal Deep Residual Shrinkage Networks
Gai Tiantian, Xiaoyong Zhao, Wang Lei, Ningning Wang · 2021 IEEE 5th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) · 2021
Multi-dimensional time series are a series of data recorded by multiple variables in the order of time. Anomaly detection refers to the detection of data that do not conform to normal expectations. Existing multi dimensional time series anomaly detection methods do not well distinguish the anomalies in the data, either in the effectiveness of the algorithm, or in the time cost. Based on the research at home and abroad, an algorithm with both algorithm effectiveness and reducing time cost advantages: Temporal Deep Residual Shrinkage Networks, TDRSN. Combining Time Convolutional Network (TCN) with Deep Residual Shrinkage Network (DRSN), adopt dilated causal convolution to expand the receptive field, introduce the soft threshold of self attention, enabling it to not only remember historical data, reduce the impact of gradient vanishing and gradient exploding, but also automatically set thresholds without expert experience, eliminate redundant features and reduce the influence of unimportant features. The experimental results show that TDRSN has a better anomaly detection effect than TCN, DRSN and other classical time series anomaly detection models.