# Stabilization of industrial processes with time series machine learning

arXiv: https://arxiv.org/abs/2506.22502 · HTML: https://mlsanity.com/paper/2506.22502/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 25 Jun 2025.

## Builds on (most cited first, 1 of 1)

- [Recurrent Neural Network Controllers Synthesis with Stability Guarantees for Partially Observed Systems](https://mlsanity.com/paper/2109.03861/) (2021)

## Abstract (arXiv)

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.