# Classification of chaotic time series with deep learning

arXiv: https://arxiv.org/abs/1908.06848 · HTML: https://mlsanity.com/paper/1908.06848/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2019.

## Built on by (most cited first, 7 of 7)

- [Hypercomplex neural network in time series forecasting of stock data](https://mlsanity.com/paper/2401.04632/) (2024)
- [Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection](https://mlsanity.com/paper/2408.16612/) (2024)
- [Detecting chaos in lineage-trees: A deep learning approach](https://mlsanity.com/paper/2106.08956/) (2021)
- [How neural networks learn to classify chaotic time series](https://mlsanity.com/paper/2306.02300/) (2023)
- [Fold Bifurcation Identification through Scientific Machine Learning](https://mlsanity.com/paper/2312.14210/) (2023)
- [Learning with Mandelbrot and Julia](https://mlsanity.com/paper/2509.00903/) (2025)
- [TopTimeNet: Topologically-assisted time-series classification model](https://mlsanity.com/paper/2609.39792/) (2026)

## Abstract (arXiv)

We use standard deep neural networks to classify univariate time series generated by discrete and continuous dynamical systems based on their chaotic or non-chaotic behaviour. Our approach to circumvent the lack of precise models for some of the most challenging real-life applications is to train different neural networks on a data set from a dynamical system with a basic or low-dimensional phase space and then use these networks to classify univariate time series of a dynamical system with more intricate or high-dimensional phase space. We illustrate this generalisation approach using the logistic map, the sine-circle map, the Lorenz system, and the Kuramoto--Sivashinsky equation. We observe that a convolutional neural network without batch normalization layers outperforms state-of-the-art neural networks for time series classification and is able to generalise and classify time series as chaotic or not with high accuracy.