MULTIVARIATE TIME SERIES ANOMALY DETECTION METHOD BASED ON LSTM AUTOENCODER ENSEMBLE
Aimed at the problem that the long-short term memory AutoEncoder(LSTM-AE)is inefficient in anomaly detection on multivariate time series(MTS),a model named LSTM-AE Ensemble(LAE)is proposed.LAE integrated multiple LSTM-AEs to reconstruct each sub-sequence of normal MTS,and treated each reconstruction error as a local feature of the MTS.A fully connected network AutoEncoder(FCAE)was used to fit the reconstruction error data,so that LAE could capture the global features of MTS data.Anomaly detection was carried out according to the reconstruction error of FCAE.Experiments on three public MTS datasets show that compared with the benchmark method,LAE has better MTS anomaly detection performance with the maximum improvement 0.058 4,0.118 4,and 0.078 6 respectively on the terms of Precision,Recall,and F1_score.
Multivariate time seriesAnomaly detectionLSTM-AEEnsemble learning
国家科技期刊平台
NSTL
万方数据
维普
