| --- |
| license: apache-2.0 |
| tags: |
| - forecasting |
| - benchmark |
| - gifteval |
| - tsfile |
| - format:tsfile |
| - timeseries |
| task_categories: |
| - time-series-forecasting |
| pretty_name: temperature_rain_with_missing (GIFT-Eval, TsFile) |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
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| # temperature_rain_with_missing (TsFile) |
| |
| Apache TsFile version of the **`temperature_rain_with_missing`** 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. |
| |
| **`temperature_rain_with_missing`** — Temperature and rainfall observations, including missing values (Monash). |
| |
| All `.tsfile` files are stored under `data/`. |
| |
| ## 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 `temperature_rain_with_missing` |
| - 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} |
| } |
| ``` |
|
|
