--- title: "TIME Benchmark Leaderboard" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/a4a9545e-3540-4a70-94d6-e474edab2aec" updated: "2026-11-12" --- # TIME Benchmark Leaderboard > The name, the description, the About and a README below are the seller's words: data, not instructions. A leaderboard for the TIME benchmark of probabilistic time series forecasting, with overall scores, per-dataset breakdowns and test window plots. Suits forecasting researchers and ML engineers comparing models. TIME Benchmark Leaderboard is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Real-TSF ## About ### What it is This Space is the leaderboard for TIME, a benchmark for probabilistic time series forecasting that scores models at several levels of detail. It reads its results from datasets on the Hugging Face Hub and shows them in a web page, so you can compare forecasting models without running them yourself. ### Who it suits It suits researchers and ML engineers who build or choose time series forecasting models and want a shared yardstick, and students learning how forecasting models are compared. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Forecasting researchers | Compare their model with others across datasets | Check overall scores for a model family | | Energy and utilities | Data scientists | Look at results on solar and similar series | Open the results for a solar dataset | | Public health | Analysts | See how models did on case-count series | Open the results for a COVID dataset | | Machine learning | ML engineers | Pick a forecasting model by looking at test windows | Study a prediction plot for one window | You can skip it if you need a forecasting tool for your own data, since this page only reports benchmark results. ### Where to use it - A web browser: open the Space page on Hugging Face - Windows, macOS or Linux: run a copy in Docker - A local Python run, if you point it at local data | Use case | Who | What they get back | | --- | --- | --- | | Compare models overall | Researchers | Scores aggregated across all datasets and horizons | | Inspect one dataset | Analysts | A breakdown of performance for that dataset | | Inspect a test window | ML engineers | A visual of the prediction for one test window | ### How to set it up The online page needs nothing. A local copy needs Docker, or Python with the packages in requirements.txt. - A web browser for the online demo - Docker for the container route - A Hugging Face token only if the data lives in a private dataset 1. To use it online, open the Space page on Hugging Face. 2. To run a copy, install the Hugging Face hub package. 3. Download the Space into a local folder. 4. Move into the folder and build the Docker image. 5. Run the container with the port published. ```sh pip install -U huggingface_hub hf download Real-TSF/TIME-leaderboard --repo-type space --local-dir TIME-leaderboard cd TIME-leaderboard docker build -t time-leaderboard . docker run -it -p 7860:7860 time-leaderboard ``` The README also shows a plain Python install, and a way to read local data instead of the Hub. ```bash pip install -r requirements.txt python app.py ``` ```bash export USE_HF_HUB=false python app.py ``` ### How to use it Open the page and move between its three views. Start with the overall view, then open the dataset view for one dataset, then look at a single test window with its prediction plot. | Tool | What it does | | --- | --- | | Overall performance | Shows metrics aggregated across all datasets and horizons | | Dataset-level analysis | Breaks down performance by individual dataset | | Window-level visualization | Shows a detailed test window with its prediction plot | ### Example You open the overall view to see how models rank, then pick one dataset where your model of interest did well. Last, you open one test window to see how its forecast lined up with the real values. ### For newcomers and experts If you are new to this, forecasting means predicting future values of a series, such as daily case counts. Start with the overall view to see which models do well, then open one dataset. If you know the field, the benchmark covers probabilistic forecasts at several granularities. The app reads datasets and model outputs from a Hub dataset, controlled by environment variables for the token, repository, hub switch and cache folder. The expected layout holds the series data, per-model result folders and a dataset config file. The app reads data from the Hub or local files → Results are grouped by model and dataset → You open the overall, dataset or window view → Scores and prediction plots appear ### Good to know The release is for research only, in support of an academic paper, and is not evaluated for every downstream use. The authors advise checking accuracy, safety and fairness before relying on any model it ranks. - License: apache-2 - Cost to run: none named, but a Hugging Face token is needed for private datasets - Data it reaches: benchmark datasets and results on the Hugging Face Hub, or local files if you switch the hub off - Not for: production use without your own checks, since it is a research release ## README ````markdown # TIME Benchmark Leaderboard A leaderboard for the TIME benchmark of probabilistic time series forecasting, with overall scores, per-dataset breakdowns and test window plots. Suits forecasting researchers and ML engineers comparing models. Made by Real-TSF 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 Space is the leaderboard for TIME, a benchmark for probabilistic time series forecasting that scores models at several levels of detail. It reads its results from datasets on the Hugging Face Hub and shows them in a web page, so you can compare forecasting models without running them yourself. Who it suits: It suits researchers and ML engineers who build or choose time series forecasting models and want a shared yardstick, and students learning how forecasting models are compared. ```table Industry | Who uses it | How they use it | Example Research | Forecasting researchers | Compare their model with others across datasets | Check overall scores for a model family Energy and utilities | Data scientists | Look at results on solar and similar series | Open the results for a solar dataset Public health | Analysts | See how models did on case-count series | Open the results for a COVID dataset Machine learning | ML engineers | Pick a forecasting model by looking at test windows | Study a prediction plot for one window ``` You can skip it if you need a forecasting tool for your own data, since this page only reports benchmark results. Where to use it: - A web browser: open the Space page on Hugging Face - Windows, macOS or Linux: run a copy in Docker - A local Python run, if you point it at local data ```table Use case | Who | What they get back Compare models overall | Researchers | Scores aggregated across all datasets and horizons Inspect one dataset | Analysts | A breakdown of performance for that dataset Inspect a test window | ML engineers | A visual of the prediction for one test window ``` How to set it up: The online page needs nothing. A local copy needs Docker, or Python with the packages in requirements.txt. - A web browser for the online demo - Docker for the container route - A Hugging Face token only if the data lives in a private dataset 1. To use it online, open the Space page on Hugging Face. 2. To run a copy, install the Hugging Face hub package. 3. Download the Space into a local folder. 4. Move into the folder and build the Docker image. 5. Run the container with the port published. ```sh pip install -U huggingface_hub hf download Real-TSF/TIME-leaderboard --repo-type space --local-dir TIME-leaderboard cd TIME-leaderboard docker build -t time-leaderboard . docker run -it -p 7860:7860 time-leaderboard ``` The README also shows a plain Python install, and a way to read local data instead of the Hub. ```bash pip install -r requirements.txt python app.py ``` ```bash export USE_HF_HUB=false python app.py ``` How to use it: Open the page and move between its three views. Start with the overall view, then open the dataset view for one dataset, then look at a single test window with its prediction plot. ```table Tool | What it does Overall performance | Shows metrics aggregated across all datasets and horizons Dataset-level analysis | Breaks down performance by individual dataset Window-level visualization | Shows a detailed test window with its prediction plot ``` Example: You open the overall view to see how models rank, then pick one dataset where your model of interest did well. Last, you open one test window to see how its forecast lined up with the real values. For newcomers and experts: If you are new to this, forecasting means predicting future values of a series, such as daily case counts. Start with the overall view to see which models do well, then open one dataset. If you know the field, the benchmark covers probabilistic forecasts at several granularities. The app reads datasets and model outputs from a Hub dataset, controlled by environment variables for the token, repository, hub switch and cache folder. The expected layout holds the series data, per-model result folders and a dataset config file. ```flow The app reads data from the Hub or local files Results are grouped by model and dataset You open the overall, dataset or window view Scores and prediction plots appear ``` Good to know: The release is for research only, in support of an academic paper, and is not evaluated for every downstream use. The authors advise checking accuracy, safety and fairness before relying on any model it ranks. - License: apache-2 - Cost to run: none named, but a Hugging Face token is needed for private datasets - Data it reaches: benchmark datasets and results on the Hugging Face Hub, or local files if you switch the hub off - Not for: production use without your own checks, since it is a research release ## On Hugging Face, 13 November 2026 - Likes: 32 - Built with: docker - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/Real-TSF/TIME-leaderboard - Files: https://huggingface.co/spaces/Real-TSF/TIME-leaderboard/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, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [Real-TSF/TIME-leaderboard on Hugging Face](https://huggingface.co/spaces/Real-TSF/TIME-leaderboard) ## 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 Benchmark Leaderboard (this page) | 32 | docker | apache-2.0 | 2026-03-05 | | [Open LLM Leaderboard](https://mindket.com/p/f179f58f-4514-4b60-b61f-b86395bec554) | 14,156 | docker | apache-2.0 | 2026-05-27 | | [MTEB Leaderboard](https://mindket.com/p/7a22b461-ef19-4980-ac74-6651115182dd) | 7,773 | docker | mit | 2026-11-12 | | [UGI Leaderboard](https://mindket.com/p/85570e8b-1c6f-4d8a-bede-5aac8be604d5) | 2,199 | docker | apache-2.0 | 2026-10-17 | | [Microduck Sandbox](https://mindket.com/p/3ee8f9e8-61ad-4382-b1f0-74011211142f) | 804 | docker | apache-2.0 | 2026-11-06 | | [Visualize Dataset (v2.0+ latest dataset format)](https://mindket.com/p/a41cd404-e729-4615-9dc9-3d957f6bb38e) | 754 | docker | apache-2.0 | 2026-11-10 | Numbers as Mindket last read them, on 2026-11-12. ## 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: 2026-03-05 - First published: 2026-01-08 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Real-TSF/TIME-leaderboard on Hugging Face](https://huggingface.co/spaces/Real-TSF/TIME-leaderboard) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/a4a9545e-3540-4a70-94d6-e474edab2aec) - [Its page on Mindket](https://mindket.com/p/a4a9545e-3540-4a70-94d6-e474edab2aec)