KEYWORDS: Education and training, Gallium nitride, Correlation coefficients, Data modeling, Sensors, Detection and tracking algorithms, Data processing, Neural networks, Machine learning, Data analysis
The production data collected by a large number of sensors in actual industrial production have multiple sources, high collection frequency and multiple dimensions, and the existing cleaning methods are difficult to accurately model the changeable time series. Therefore, a high dimensional time series cleaning method based on generative adversarial network and spatial correlation is proposed. Firstly, the time series with multiple dimensions are piecewise aggregated data processing. Secondly, the correlation analysis is carried out by mathematical statistical method, and the weighted repair is made after the abnormal value is found in the sequence. Finally, the long and short term memory cycling neural network training data set was used to complete the data cleaning, and the accuracy rate, recall rate and F1 score were calculated to evaluate the abnormal detection performance. The experimental results show that the cleaning efficiency and accuracy of this method are significantly improved.
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