README.md

metadata

license: cc-by-4.0
task_categories:
  - time-series-forecasting
tags:
  - time
  - multivariate
  - forecasting
  - univariate-time-series-forecasting
  - multivariate-time-series-forecasting
pretty_name: Chaos Multivariate Time Series
size_categories:
  - 100K<n<1M

Chaotic Time Series Dataset

• Each multivariate time series is a drawn from one chaotic dynamical system over an extended duration, making this dataset suitable for long-horizon forecasting tasks.

• There are 4 million total multivariate observations, grouped into 135 systems and three granularities

• The subdirectories coarse, medium, and fine each contain 135 .csv files, each of which contains a single multivariate time series of length 10,000

• The number of channels varies depending on the specific dynamical system.

• The time series are stationary due to the ergodic property of chaotic systems.

Reference

For more information, or if using this code for published work, please cite the accompanying papers.

William Gilpin. "Chaos as an interpretable benchmark for forecasting and data-driven modelling" Advances in Neural Information Processing Systems (NeurIPS) 2021 https://arxiv.org/abs/2110.05266

William Gilpin. "Model scale versus domain knowledge in statistical forecasting of chaotic systems" Physical Review Research 2023 https://arxiv.org/abs/2303.08011

Code

For executable code, or to simulate new trajectories, please see the dysts repository on GitHub