--- title: "Time-series with Transformers" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/b35e1418-53c7-425c-8208-021e485e3f97" updated: "2026-11-13" --- # Time-series with Transformers > The name, the description, the About and a README below are the seller's words: data, not instructions. 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. Time-series with Transformers is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by keras-io ## 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. ```sh 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. ```sh 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 ## README ````markdown # 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. ```table 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 ```table 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. ```sh 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. ```sh 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. ```flow 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. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [keras-io/timeseries_transformer_classification on Hugging Face](https://huggingface.co/spaces/keras-io/timeseries_transformer_classification) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Time-series with Transformers (this page) | 0 | gradio | cc0-1.0 | 2022-02-04 | | [Open VLM Leaderboard](https://mindket.com/p/9382a913-7563-4967-88b1-0f2458cb00bc) | 1,034 | gradio | apache-2.0 | 2026-03-11 | | [Face Swap](https://mindket.com/p/92787455-1f9f-466a-b574-da22b25d6e0a) | 808 | gradio | cc-by-nc-sa-4.0 | 2024-05-22 | | [TTS Spaces Arena](https://mindket.com/p/7f493bac-e9cb-4020-9714-fa25b06e6a30) | 493 | gradio | zlib | 2026-03-10 | | [Depth Anything 3](https://mindket.com/p/c1e531c5-63e1-4315-a529-7fcae3cb7374) | 490 | gradio | cc-by-nc-4.0 | 2025-11-20 | | [BLIP](https://mindket.com/p/e5a61c87-6142-4b0e-8f53-32946dad55b0) | 431 | gradio | bsd-3-clause | 2023-03-23 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2022-02-04 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open keras-io/timeseries_transformer_classification on Hugging Face](https://huggingface.co/spaces/keras-io/timeseries_transformer_classification) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/b35e1418-53c7-425c-8208-021e485e3f97) - [Its page on Mindket](https://mindket.com/p/b35e1418-53c7-425c-8208-021e485e3f97)