Public notebooks and utilities for working with Time Series Foundation Models (TSFM)
The core TSFM time series models have been made available on Hugging Face -- details can be found here.
The current Python versions supported are 3.9 and 3.10.
First clone the repository:
git clone git@github.com:IBM/tsfm.git
cd tsfmSeveral notebooks are provided in the notebooks folder. They allow you to perform pre-training and finetuning on the models.
To install use pip:
pip install ".[notebooks]"- Getting started with
PatchTSMixer[Try it out] - Transfer learning with
PatchTSMixer[Try it out] - Transfer learning with
PatchTST[Try it out] - Getting started with
TinyTimeMixer (TTM)Try it out
Run the TTM tutorial in Google Colab, and quickly build a forecasting application with pre-trained TSFM models.
The demo presented at NeurIPS 2023 is available in tsfmhfdemos. This demo requires you to have pre-trained and finetuned models in place (we plan to release these at later date). To install the requirements use pip:
pip install ".[demos]"If you encounter an issue with this project, you are welcome to submit a bug report. Before opening a new issue, please search for similar issues. It's possible that someone has already reported it.
The intention of this repository is to make it easier to use and demonstrate IBM Research TSFM components that have been made available in the Hugging Face transformers library. As we continute to develop these capabilities we will update the code here.
IBM Public Repository Disclosure: All content in this repository including code has been provided by IBM under the associated open source software license and IBM is under no obligation to provide enhancements, updates, or support. IBM developers produced this code as an open source project (not as an IBM product), and IBM makes no assertions as to the level of quality nor security, and will not be maintaining this code going forward.
