Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Conformal prediction for multi-dimensional time series by ellipsoidal sets
Authors: Chen Xu, Hanyang Jiang, Yao Xie
ICML 2024 | Venue PDF | 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. |