Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
pyts: A Python Package for Time Series Classification
Authors: Johann Faouzi, Hicham Janati
JMLR 2020 | Venue PDF | LLM Run Details | Input Tokens: 7,328 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,144 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Table 2: Accuracy scores on the test set for the BOSS transformer followed by a one-nearest neighbor classifier with the BOSS metric on several data sets. |
| Researcher Affiliation | Academia | Johann Faouzi EMAIL Aramis Lab, INRIA Paris Brain and Spine Institute 75013 Paris France; Hicham Janati EMAIL Parietal team, INRIA Saclay Neurospin, Bˆat 145 CEA Saclay 91191 Gif sur Yvette France |
| Pseudocode | Yes | Listing 1: Code snippet illustrating pyts s intuitive API on a classification example. |
| Open Source Code | Yes | pyts is an open-source Python package for time series classification. ... Source code and documentation can be downloaded from https://github.com/johannfaouzi/pyts. |
| Open Datasets | Yes | X_train , X_test , y_train , y_test = load_gunpoint(return_X_y=True); Table 2: Accuracy scores on the test set for the BOSS transformer followed by a one-nearest neighbor classifier with the BOSS metric on several data sets. Adiac ECG200 Gun Point Middle Phalanx TW Plane (Sch afer, 2015) |
| Dataset Splits | Yes | X_train , X_test , y_train , y_test = load_gunpoint(return_X_y=True) |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory) are mentioned in the paper. |
| Software Dependencies | Yes | Packages (versions): pyts (0.10.0), sktime (0.3.1), tslearn (0.2.5), seglearn (1.1.0), tsfresh (0.13.0), cesium (0.9.10). |
| Experiment Setup | Yes | clf = BOSSVS(window_size =28) |