DTDR–ALSTM: Extracting dynamic time-delays to reconstruct multivariate data for improving attention-based LSTM industrial time series prediction models

Abstract

Taking advantage of varying degrees of attention on specific features, attention-based long short-term memory (ALSTM) networks have made inroads into the industrial multivariate time series prediction sector recently. However, conventional ALSTM models usually employ static time-delays to constant select input/output pairings of multivariate data, which apparently ignores dynamics of transmission time between industrial process variables and degrades the prediction performance by incorrectly extracting process characteristics. In response to this problem, this paper proposes a novel approach to extracting dynamic time-delays to reconstruct (DTDR) multivariate data for an improved ALSTM prediction model. Therein, the temporal locations and spans of multivariate data are adaptively tailored to input/output pairings of the ALSTM network according to the dynamic time-delays. Specifically, the multivariate data can be accurately matched in temporal positions, and the data information in the original temporal spans with Status transfer time abnormal are replaced. Consequently, this prediction model not only appropriately utilizes dynamics between the predicting and correlated variables, but also makes better attentions on key features extracted from optimum data. Applied to industrial distillation and methanol production processes, the proposed method demonstrates the capability of significantly improving network training speeds as well as prediction accuracies in contrast to static time-delay based ALSTM and LSTM models, expecting even more applications.