论文标题
PYTORCH的基准时间序列数据集 - 火炬套件
Benchmark time series data sets for PyTorch -- the torchtime package
论文作者
论文摘要
电子健康记录数据模型的开发是一个积极研究的领域，其中包含少数公共基准数据集。研究人员通常编写自定义数据处理代码，但这会阻碍可重复性并引入错误。 Python软件包TORCHTIME提供了常用Physionet和UEA和UCR时间序列分类存储库数据集的可重复实现。提供了用于处理不规则的不规则时间序列的不规则时间序列的特征。它旨在简化对生理学数据的访问，并在这一令人兴奋的研究领域中对模型进行公平的比较。
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.
