Conformal prediction for multi-dimensional time series by ellipsoidal sets
Authors: Chen Xu, Hanyang Jiang, Yao Xie
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we demonstrate that Multi Dim SPCI maintains valid coverage on a wide range of multivariate time series while producing smaller prediction regions than CP and non-CP baselines. |
| Researcher Affiliation | Academia | 1H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology. |
| Pseudocode | Yes | Algorithm 1 Multi-dimensional SPCI (Multi Dim SPCI) |
| Open Source Code | Yes | Code is available at https: //github.com/hamrel-cxu/Multi Dim SPCI. |
| Open Datasets | Yes | We consider three real multivariate time-series datasets. The first wind dataset considers wind speed in meters per second at different wind farms (Zhu et al., 2021) [...] The second solar dataset considers solar radiation in Diffused Horizontal Irradiance (DHI) units at different solar sensors (Zhang et al., 2021) [...] The third traffic dataset considers traffic flow collected at different traffic sensors (Xu & Xie, 2021a). |
| Dataset Splits | Yes | The initial 80K samples {Yt} are training data; the remaining 20K samples are test data. [...] The first 85% data are used for training, and the remaining 15% are used for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. It only mentions 'running experiments' in general terms. |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies, libraries, or frameworks used in the experiments (e.g., Python, PyTorch, TensorFlow, etc.). |
| Experiment Setup | Yes | In all our experiments, the value of ρ used in (4) is set to be 0.001. [...] For simplicity, we only consider the global covariance matrix in (3) rather than its local variant (A.1), which would bring further improvements. [...] We recommend setting k = 0.1T and λ = 0.95 to capture local variations effectively. [...] The multivariate linear regression method is used as the point predictor. |