Industrial Time Series Data Cleaning Using Generative LSTM and Adaptive Confidence Interval

In this paper, a method of industrial time series data cleaning using generative LSTM model and adaptive confidence interval is proposed. Firstly, the generative LSTM model is used to predict the probability distribution of industrial time series data, and then the confidence boundary of data anomalies is calculated according to the adaptive confidence interval method, so as to judge whether the data is abnormal or not. This paper uses network open data to complete data cleaning experiment, and the precision rate and recall rate of abnormal data detection are above 96%. The comparison with other anomaly detection algorithms also proves the effectiveness of the proposed algorithm in industrial time series data cleaning.