Stabilization of industrial processes with time series machine learning

Matvei Anoshin; Olga Tsurkan; Vadim Lopatkin; Leonid Fedichkin

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arXiv · 2506.22502

Stabilization of industrial processes with time series machine learning

Matvei Anoshin·Olga Tsurkan·Vadim Lopatkin·Leonid Fedichkin

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Abstract

The stabilization of time series processes is a crucial problem that is ubiquitous in various industrial fields. The application of machine learning to its solution can have a decisive impact, improving both the quality of the resulting stabilization with less computational resources required. In this work, we present a simple pipeline consisting of two neural networks: the oracle predictor and the optimizer, proposing a substitution of the point-wise values optimization to the problem of the neural network training, which successfully improves stability in terms of the temperature control by about 3 times compared to ordinary solvers.

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Matvei Anoshin, Olga Tsurkan, Vadim Lopatkin, Leonid Fedichkin. 2025-06-25. Stabilization of industrial processes with time series machine learning. https://arxiv.org/abs/2506.22502

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