Prediction and clustering models based on multivariate parameters

Abstract

In the multi-parameter sequence in the industrial electrolyzer, in order to solve the problem that the traditional method is difficult to predict the nonlinear features and obtain the hidden feature information in the sequence, this paper uses the VARMA model to fit the multi-parameter features and combines the Time2Vec vector to embed the time form as the neural network. Augmented data sources for automated feature engineering and generalization of deep learning techniques; multivariate parameters were dimensionally reduced and KS tests were used to capture correlations in order to explore relationships between electrolyzers. The experimental results show that the model is superior to other comparative models in terms of computational efficiency, accuracy, and network structure, which verifies the effectiveness of its prediction in the multi-parameter field.