README.md

---
license: apache-2.0
tags:
- timeseries
- tsfile
- time-series
- forecasting
- benchmark
- gifteval
task_categories:
- time-series-forecasting
pretty_name: temperature_rain_with_missing (GIFT-Eval, TsFile)
---

# 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}
}
```