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--- |
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license: cc-by-4.0 |
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task_categories: |
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- time-series-forecasting |
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tags: |
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- time |
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- multivariate |
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- forecasting |
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- univariate-time-series-forecasting |
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- multivariate-time-series-forecasting |
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pretty_name: Chaos Multivariate Time Series |
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size_categories: |
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- 1M<n<10M |
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--- |
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### Chaotic Time Series Dataset |
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Multivariate time series from chaotic dynamical systems. |
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+ 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. |
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+ There are 4 million total multivariate observations, grouped into 135 systems and three granularities |
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+ The subdirectories `coarse`, `medium`, and `fine` each contain 135 `.csv` files, each of which contains a single multivariate time series of length 10,000 |
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+ The number of channels varies depending on the specific dynamical system. |
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+ The time series are stationary due to the ergodic property of chaotic systems. |
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## Reference |
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For more information, or if using this code for published work, please cite the accompanying papers. |
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> 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 |
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> William Gilpin. "Model scale versus domain knowledge in statistical forecasting of chaotic systems" Physical Review Research 2023 https://arxiv.org/abs/2303.08011 |
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## Code |
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For executable code, or to simulate new trajectories, please see the [dysts repository on GitHub](https://github.com/williamgilpin/dysts) |
