The aim of torchtime is to apply PyTorch to the time series domain. By supporting PyTorch, torchtime follows the same philosophy of providing strong GPU acceleration, having a focus on trainable features through the autograd system, and having consistent style (tensor names and dimension names). Therefore, it is primarily a machine learning library and not a general signal processing library. The benefits of PyTorch can be seen in torchtime through having all the computations be through PyTorch operations which makes it easy to use and feel like a natural extension.
- Support time series I/O (Load files, Save files)
- Load a variety of time series formats, such as
ts,arff,dvi,dxd, into a torch Tensor
- Load a variety of time series formats, such as
- Dataloaders for common time series datasets
- Common time series transforms
Please refer to https://pytorchtime.com/docs/stable/installation.html for installation and build process of torchtime.
API Reference is located here: http://pytorchtime.com/docs/stable/
Please refer to CONTRIBUTING.md
If you find this package useful, please cite as:
@software{scharf2024torchtime,
author = {Vincent Scharf},
title = {torchtime},
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.13832394},
url = {https://github.com/VincentSch4rf/torchtime}
}This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.
If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!