Shape analysis for time series
Authors: Thibaut Germain, Samuel Gruffaz, Charles Truong, Alain Durmus, Laurent Oudre
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We showcase the advantages of our representation compared to existing methods using synthetic data and real-world examples motivated by biomedical applications. |
| Researcher Affiliation | Academia | Thibaut Germain1 Centre Borelli, ENS Paris-Saclay 4 av. des sciences, 91190 Samuel Gruffaz1 Centre Borelli, ENS Paris-Saclay 4 av. des sciences, 91190 Charles Truong1 Centre Borelli, ENS Paris-Saclay 4 av. des sciences, 91190 Laurent Oudre1 Centre Borelli, ENS Paris-Saclay 4 av. des sciences, 91190 Alain Durmus CMAP, CNRS, Ecole polytechnique Institut Polytechnique de Paris 91120 Palaiseau, France |
| Pseudocode | No | The paper describes algorithms and equations but does not provide a formal pseudocode block or algorithm environment labeled as such. |
| Open Source Code | Yes | The source code is available on Github4. https://github.com/thibaut-germain/TSLDDMM |
| Open Datasets | Yes | We selected 15 shape-based datasets (7 univariates and 8 multivariates) from the from the University of East Anglia (UEA) and the University of California Riverside (UCR) Time Series Classification Repository8 [15, 3]. |
| Dataset Splits | Yes | Spilt the dataset in train 75%, validation 15%, and test 15%. |
| Hardware Specification | Yes | All experiments were performed on a Debian 6.1.69-1 server with NVIDIA RTX A2000 12GB GPU, Intel(R) Xeon(R) Gold 5220R CPU @ 2.20GHz, and 250 GB of RAM. |
| Software Dependencies | No | The paper mentions specific libraries like "JAX library" and "OPTAX library" but does not include specific version numbers for these or other key software components, which is required for reproducibility. |
| Experiment Setup | Yes | The optimization hyperparameter details are given in Appendix E.1. By default, we set nb_steps to 400 and ηM to 0.1. To learn TS-LDDMM (resp. LDDMM) representations, the velocity field kernel KG is set to (c0, c1, σT,0, σT,1, σx) = (1, 0.1, 0.33 l, 1, nd), (resp. (σT , σx) = (0.33 l, nd)) where l is the average time series length and nd the number of dimensions. |