# Benchmark time series data sets for PyTorch -- the torchtime package

arXiv: https://arxiv.org/abs/2207.12503 · HTML: https://mlsanity.com/paper/2207.12503/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 45 references that are in the map.
- First posted on arXiv 25 Jul 2022.

## Builds on (most cited first, 5 of 5)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://mlsanity.com/paper/1606.01865/) (2016)
- [Neural Controlled Differential Equations for Irregular Time Series](https://mlsanity.com/paper/2005.08926/) (2020)
- [Set Functions for Time Series](https://mlsanity.com/paper/1909.12064/) (2019)
- [sktime: A Unified Interface for Machine Learning with Time Series](https://mlsanity.com/paper/1909.07872/) (2019)

## Built on by (most cited first, 2 of 2)

- [DuETT: Dual Event Time Transformer for Electronic Health Records](https://mlsanity.com/paper/2304.13017/) (2023)
- [EBES: Easy Benchmarking for Event Sequences](https://mlsanity.com/paper/2410.03399/) (2024)

## 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.