Stabilization of industrial processes with time series machine learning

An application of machine learning to the industrial processes stabilization is an open problem which promises a huge potential benefit to the such critical industries as metals and energy development if solved. Classical optimization methods, such as finite-horizon markov decision processes [1], non-linear programming reformulation of control [2] and point-wise optimization [3] are frequently employed in order to achieve better stability of time series process, successfully improving production quality, minimizing expenses and manufacturing devices deficiency with near-future planing or real-time optimization. Machine learning, known for its prominent results in solution of enterprise problems [4], became widely applied to the time series prediction and generation after recent advances in such fields as natural language processing, due to the similarity aforementioned tasks in their time dependent recurrent nature [5]. Thus, contemporary time series modeling is performed with long short-term memory (LSTM) models [6] and Transformers [7], incorporating different attention strategies. Currently, state-of-the-art approaches to ML-driven optimization include an application of reinforcement learning, but for time series problems, the usual focus stays on approximation of the industrial process as a dynamic system on the basis of recurrent neural network (RNN), with such methods as recurrent stabilization control [8, 9].