Foundation Models for Time Series.
ibm-granite/granite-tsfm is ranked 6,051 with 912 stars.
Same-category comparison
ibm-granite/granite-tsfm is ranked 6,051 among these 8 peers; huawei-bayerlab/marigold-v2 is ranked 6,050.
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| huawei-bayerlab/marigold-v2 | 6,050 | - Language:
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Key parameters and specifications
Key parameters and specifications| Parameter | Value |
|---|
| Language | Jupyter Notebook |
|---|
| Category | Models |
|---|
| Subcategory | Domain-specific models |
|---|
| Stars | 912 |
|---|
| Forks | 283 |
|---|
| Contributors | 27 |
|---|
| Created date | 2023-02-27 |
|---|
| Updated date | 2026-10-16 |
|---|
| Stars in 1 day | 1 |
|---|
| Stars in 7 days | 3 |
|---|
| Rank | 6,051 |
|---|
| Previous rank | 6,221 |
|---|
| Rank change | 170 |
|---|
| 7-day stars rank | 7,157 |
|---|
| Previous 7-day stars rank | 7,184 |
|---|
| 7-day stars rank change | 27 |
|---|
| Total stars rank | 11,943 |
|---|
| Previous total stars rank | 11,929 |
|---|
| Total stars rank change | -14 |
|---|
| Country | USA |
|---|
README summary
TSFM: Time Series Foundation Models Public notebooks, utilities, and serving components for working with Time Series Foundation Models (TSFM). Additional details can be found in our wiki. Information on the services component can be found here. Python Version The current Python versions supported are 3. Initial Setup First clone the repository: 📕 Notebooks Installation Several notebooks are provided in the folder. They allow you to perform pre-training and finetuning on the models. To install use : 🔗 Links to the notebooks Getting
started with [[Try it out]]( Transfer learning with [[Try it out]]( Transfer learning with [[Try it out]]( Getting started with [[Try it out]]( full benchmarking scripts and results are available [[here]]( example notebook is available [[here]]( example notebook is available [[here]]( 📗 Google Colab Tutorials Run the TTM tutorial in Google Colab, and quickly build a forecasting application with the pre-trained TSFM models. TTM Colab Tutorial 💻 Demos Installation The demo presented at NeurIPS 2023 is available in . This demo requires
you to have pre-trained and finetuned models in place (we plan to release these at a later date). To install the requirements use : 🪲 Issues 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. 🌏 Wiki Wiki Page Notice The intention of this repository is to make it easier to use and demonstrate Granite TimeSeries components
that have been made available in the Hugging Face transformers library. As we continue 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.
Frequently asked questions
How many stars and forks are recorded?
ibm-granite/granite-tsfm has 912 stars and 283 forks.
What is the repository rank?
ibm-granite/granite-tsfm is ranked 6,051.
What language and category are recorded?
ibm-granite/granite-tsfm has 912 stars, uses Jupyter Notebook and belongs to Models.
Which repository is closest in rank?
ibm-granite/granite-tsfm is compared with huawei-bayerlab/marigold-v2, ranked 6,050 with 913 stars.
TSFM: Time Series Foundation Models
Public notebooks, utilities, and serving components for working with Time Series Foundation Models (TSFM).
The core TSFM time series models have been made available on Hugging Face -- check out the granite time series collection here. Additional details can be found in our
wiki. Information on the services component can be found here.
Python Version
The current Python versions supported are 3.10, 3.11, 3.12, 3.13.
Initial Setup
First clone the repository:
git clone "https://github.com/ibm-granite/granite-tsfm.git"
cd granite-tsfm
📕 Notebooks Installation
Several 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]"
🔗 Links to the 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]
TTM full benchmarking scripts and results are available [here]FlowState example notebook is available [here]PatchTST-FM example notebook is available [here]
📗 Google Colab Tutorials
Run the TTM tutorial in Google Colab, and quickly build a forecasting application with the pre-trained TSFM models.
💻 Demos Installation
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 a later date). To install the requirements use pip:
pip install ".[demos]"
🪲 Issues
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.
🌏 Wiki
Wiki Page
Notice
The intention of this repository is to make it easier to use and demonstrate Granite TimeSeries components that have been made available in the Hugging Face transformers library. As we continue 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.