| --- |
| license: apache-2.0 |
| tags: |
| - forecasting |
| - benchmark |
| - gifteval |
| - tsfile |
| - format:tsfile |
| - timeseries |
| task_categories: |
| - time-series-forecasting |
| pretty_name: ett2 (GIFT-Eval, TsFile) |
| size_categories: |
| - n<1K |
| --- |
| |
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| # ett2 (TsFile) |
|
|
| Apache TsFile version of the **`ett2`** subset of |
| [GIFT-Eval](https://huggingface.co/datasets/Salesforce/GiftEval). |
|
|
| ## Overview |
|
|
| GIFT-Eval is a benchmark for general time-series forecasting, covering 23 datasets |
| (≈144,000 series and 177M data points) across seven domains, ten frequencies, and a |
| range of forecast horizons. This repository contains a single subset of that benchmark. |
|
|
| **`ett2`** — Electricity Transformer Temperature, station 2 (ETT) load and oil-temperature series. |
|
|
| The `.tsfile` files are organized by the original GIFT-Eval frequency under `data/`: |
|
|
| - `data/15T/`: 1 `.tsfile` files |
| - `data/D/`: 1 `.tsfile` files |
| - `data/H/`: 1 `.tsfile` files |
| - `data/W/`: 1 `.tsfile` files |
|
|
| ## Schema (TsFile structure) |
|
|
| - **Time** (INT64, milliseconds) — the timestamp of each observation. |
| - Each series is stored as a TsFile device; per-series identifiers from GIFT-Eval are |
| carried as TAG columns, and the observed values are FIELD columns. |
|
|
| ## Usage |
|
|
| Read the `.tsfile` files with the Apache TsFile Java or Python SDK. |
|
|
| ## Source & license |
|
|
| - Original dataset: [`Salesforce/GiftEval`](https://huggingface.co/datasets/Salesforce/GiftEval) — subset `ett2` |
| - Paper: [GIFT-Eval (arXiv:2410.10393)](https://arxiv.org/abs/2410.10393) |
| - Code: https://github.com/SalesforceAIResearch/gift-eval |
| - License: apache-2.0 |
|
|
| If you use this data, please cite GIFT-Eval: |
|
|
| ```bibtex |
| @article{aksu2024giftevalbenchmarkgeneraltime, |
| title={GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation}, |
| author={Taha Aksu and Gerald Woo and Juncheng Liu and Xu Liu and Chenghao Liu and Silvio Savarese and Caiming Xiong and Doyen Sahoo}, |
| journal={arXiv preprint arXiv:2410.10393}, |
| year={2024} |
| } |
| ``` |
|
|
