Multi-level deep domain adaptive adversarial model based on tensor-train decomposition for industrial time series forecasting

Abstract The polyester industry is a complex process industry, building a time series prediction model for new production lines or equipment with new sensors can be challenging due to a lack of historical data. The time-series data collected from sensors cross-production-line often exhibit varying distributions. Current domain adaptation (DA) approaches in data-driven time series forecasting primarily concentrate on adjusting either the features or the models, neglecting the intricacies of industrial time series data. Furthermore, constructing deep neural networks for industrial data necessitates substantial computational resources and runtime due to their large and high-dimensional nature. In order to tackle these obstacles, we propose a novel Multi-level deep domain adaptive adversarial model based on tensor-train decomposition (TT-MDAM). Our model aims to strike a dynamic balance between prediction accuracy and runtime efficiency. By integrating multiple perspectives at the feature, trend, and model levels, we leverage DA to enhance the prediction accuracy of our model in the target domain. Additionally, by analyzing the weight matrix of the neural network, we generate a low-rank model to improve operational efficiency. The application of the proposed TT-MDAM approach to both the three-phase flow facility process (TPFF) dataset and a real-world polyester esterification process dataset reveals promising results, outperforming state-of-the-art methodologies in terms of prediction performance. The results indicate that the approach provides a viable solution for building time series prediction models in industrial processes with new equipment or production lines.