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..
Seglearn: A Python Package for Learning Sequences and Time Series
Authors: David M. Burns, Cari M. Whyne
JMLR 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The performance comparison was conducted using our human activity recognition data set with 140 multivariate time series with 6 channels sampled uniformly at 50 Hz and 7 activity classes. Classification accuracy was measured on 35 series held out for testing, and 105 used for training. seglearn, cesium-ml, and tsfresh were tested using the sklearn implementation of the SVM classifier... Classification accuracy was identical between cesium-ml, tsfresh, and seglearn... though seglearn significantly outperformed the other packages in terms of computation time. (Table 2: Comparison of time series learning package performance...) |
| Researcher Affiliation | Academia | David M. Burns EMAIL Sunnybrook Research Institute Cari M. Whyne EMAIL Sunnybrook Research Institute 2075 Bayview Ave. Room S620. Toronto, ON, Canada. M4N 3M5. |
| Pseudocode | No | The paper contains Figure 1 which shows "Example seglearn pipelines" with block diagrams and an example Python code snippet in Section 5. It does not contain any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | seglearn is an open-source Python package for performing machine learning on time series or sequences. ... Source code and documentation can be downloaded from https://github.com/dmbee/seglearn. |
| Open Datasets | Yes | A human activity recognition data set (Burns et al., 2018) consisting of inertial sensor data recorded by a smartwatch worn during shoulder rehabilitation exercises is provided with the source code to demonstrate the features and usage of the seglearn package. |
| Dataset Splits | Yes | Classification accuracy was measured on 35 series held out for testing, and 105 used for training. |
| Hardware Specification | Yes | The testing was performed using an Intel Core i7-4770 testbed with 16 GB of installed memory, on Linux Mint 18.3 with Python 2.7.12. |
| Software Dependencies | Yes | The testing was performed using an Intel Core i7-4770 testbed with 16 GB of installed memory, on Linux Mint 18.3 with Python 2.7.12. tslearn v0.1.18.4, cesium-ml v0.9.6, tsfresh v0.11.1 and seglearn v1.0.2. |
| Experiment Setup | Yes | Classification performance of the global alignment kernel SVM (GAK-SVM) implemented in tslearn was poor on our data set, even following hyper-parameter optimization of gamma by grid search over the log space [10 4, 104]. |