Time-series with Transformers

Time-series with Transformers

Lets you try a transformer model that classifies time series in a Gradio page, with two sample CSV files included. Aimed at students and developers who work with sequence data.

Made by keras-io on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face.

About

What it is: This Gradio demo from keras-io wraps one model, keras-io/timeseries_transformer_classification. The name says it classifies time series with a transformer, and two sample CSV files sit in the repository. You use it in a browser, or you run a copy on your own computer.

Who it suits: It suits students and developers who work with sequence data and want to see a transformer classifier at work. Teachers can show it as a live demo.

Industry | Who uses it | How they use it | Example
Education | Machine learning students | Try a time-series classifier without writing code | Open the Space and use the sample data
Research | Data scientists | See how a finished model is wired into a demo | Read app.py next to the sample files
Software | Python developers | Run a copy locally and adapt it | Download the Space and start app.py

Skip it if you need a training script or forecasting, because the name points to classification only.

Where to use it:

• A web browser: go to the Space on Hugging Face and try it with no install
• Windows, macOS or Linux: run a copy with Python

Use case | Who | What they get back
Try the model | Students | A class shown on the page for the series they give
Read the code | Developers | The app.py file that connects the model to a Gradio page
Reuse the samples | Instructors | Two CSV files they can use in a class demo

How to set it up: You need a few things in place first.

• Python with pip, on Windows, macOS or Linux
• An internet connection to fetch the files from Hugging Face

1. Install the Hugging Face hub package.
2. Download the Space into a local folder.
3. Move into that folder.
4. Install Gradio and the packages listed in requirements.txt.
5. Start the app.

pip install -U huggingface_hub
hf download keras-io/timeseries_transformer_classification --repo-type space --local-dir timeseries_transformer_classification
cd timeseries_transformer_classification
pip install gradio
pip install -r requirements.txt
python app.py

How to use it: Online, use the Gradio interface the Space page shows. On your own computer, run the main file from the project folder and open the page it serves in your browser.

python app.py

Example: You run python app.py inside the project folder. The Gradio page starts, you give it a series such as one of the two sample CSV files, and the page shows the class the model returns for it.

For newcomers and experts: If this is new to you, start online. Go to the Space and try the sample data before you install anything. It is the quickest way to see a time-series classifier working.

If you know the field, note that the Space names a single model and app.py loads it. The sample CSV files suggest the demo reads a series from a file. This Space looks like inference only, going by its listed files, so it gives no training steps or accuracy figures.

Open the Space or run app.py
The Gradio page loads
You give it a time series
The named model classifies it
The result appears on the page

Good to know: The source gives no accuracy numbers or input format, so test it on your own series before you rely on it.

• License: cc0-1
• Cost to run: none named, no API key, account or paid service
• Data it reaches: the files in the repository and the model it names, with nothing else listed

On Hugging Face, 13 November 2026

• Likes: 0
• Built with: gradio
• License: cc0-1.0

Links

• Space: https://huggingface.co/spaces/keras-io/timeseries_transformer_classification
• Files: https://huggingface.co/spaces/keras-io/timeseries_transformer_classification/tree/main

Try it

Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself.