Benchmark time series data sets for PyTorch -- the torchtime package
Not on the map. Why?
Abstract (arXiv)
The development of models for Electronic Health Record data is an area of active research featuring a small number of public benchmark data sets. Researchers typically write custom data processing code but this hinders reproducibility and can introduce errors. The Python package torchtime provides reproducible implementations of commonly used PhysioNet and UEA & UCR time series classification repository data sets for PyTorch. Features are provided for working with irregularly sampled and partially observed time series of unequal length. It aims to simplify access to PhysioNet data and enable fair comparisons of models in this exciting area of research.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 5 of its 45 references that are in the map. First posted on arXiv 25 Jul 2022.
Builds on
5 of 45 references.
References · 5
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2016Recurrent Neural Networks for Multivariate Time Series with Missing Valuescited by 548
- 2020Neural Controlled Differential Equations for Irregular Time Seriescited by 501
- 2019Set Functions for Time Seriescited by 137
- 2019sktime: A Unified Interface for Machine Learning with Time Seriescited by 98
Built on by
Citing papers per year: 2023: 1; 2024: 1.
2 papers.
- 2023DuETT: Dual Event Time Transformer for Electronic Health Recordscited by 17
- 2024EBES: Easy Benchmarking for Event Sequencescited by 6
- 2024EBES: Easy Benchmarking for Event Sequences4 Oct 2024
- 2023DuETT: Dual Event Time Transformer for Electronic Health Records25 Apr 2023