| --- |
| 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: |
| - 1M<n<10M |
| --- |
| |
| ### Chaotic Time Series Dataset |
|
|
| Multivariate time series from chaotic dynamical systems. |
|
|
| + 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](https://github.com/williamgilpin/dysts) |
