Self-attention Mechanism-based Prediction and Anomaly Detection of Time Series
With the progress of the Internet of Things(IoT),time series data can be collected in large quantities,and it is becoming more and more important to accurately predict time series data and reliably detect anomalies.This paper propo-ses a linear time series prediction method based on self-attention mechanism,which can simultaneously extract the inter-sequence feature correlations.The introduction of self-attention mechanism in linear prediction model facilitates accurate extraction of key information from multi-dimensional time series data,and hence,improving prediction accuracy and reali-zing anomaly detection.The performance of linear prediction method before and after the introduction of self-attention mechanism was experimentally compared from the perspective of engineering and algorithm,and the modified method a-chieved better prediction performance and anomaly detection accuracy on SMD data set and MSL/SMAP data set,exhibi-ted significantly improved accuracy and robustness,and thereby was potentially promising in state prediction and anomaly detection under industrial control conditions.
time seriesanomaly detectionself-attention mechanismlinear prediction
万方数据
维普
