README.md

---
configs:
- config_name: default
  data_files:
  - split: test
    path: meta_data.jsonl
license: cc-by-nc-sa-4.0
task_categories:
- time-series-forecasting
- question-answering
language:
- en
tags:
- time series
- timeseries
- audio
- benchmark
- time series Reasoning
- time series Classification
- time series QA
- time series Anomaly Detection
- classification
- anomaly detection
size_categories:
- 10K<n<100K
pretty_name: 'SciTS: Scientific Time Series Understanding and Generation with LLMs'
---

# SciTS: Scientific Time Series Understanding and Generation with LLMs


This repository contains the official dataset for [**SciTS: Scientific Time Series Understanding and Generation with LLMs** (ICLR 2026)](https://openreview.net/forum?id=5YXccEP6uc). SciTS is a large-scale benchmark designed to evaluate the capabilities of large language models on complex scientific time series data. It spans 12 scientific disciplines, 43 distinct tasks, and includes 54,023 instances.

![SciTS](./process/data_overview.jpg)

## Dataset Structure

The benchmark is organized into a main `meta_data.jsonl` file, a `process` directory for handling restricted datasets, and 38 individual dataset folders. Each folder is named using the convention: `Domain-DatasetName-Scene-Task`.

```
├── process/
│   ├── process_ETT.py
│   ├── process_iNaturalist.py
│   ├── infer_template.py
│   ├── eval.py
│   └── requirements.txt
├── Domain-DatasetName-Scene-Task_1/
│   ├── raw_input_data/
│   └── raw_gt_data/ (for generation tasks)
├── Domain-DatasetName-Scene-Task_2/
│   └── raw_input_data/
...
├── Domain-DatasetName-Scene-Task_38/
│   ├── raw_input_data/
│   └── raw_gt_data/
└── meta_data.jsonl
```

- **`process/`**: Contains utility scripts, including `process_ETT.py` and `process_iNaturalist.py` for processing restricted datasets which cannot be released directly due to license restrictions, `infer_template.py` as an inference template, `eval.py` for evaluation, and `requirements.txt` for dependency installation.
- **`Dataset Folders`**: Each of the 38 folders contains the raw time series data for a specific dataset. `raw_input_data` holds the input signals, while `raw_gt_data` (present only for generation tasks) holds the ground truth output signals.
- **`meta_data.jsonl`**: A JSON Lines file containing metadata for every instance in the benchmark. Each line corresponds to one data sample.

### Dataset Collection

The 38 released datasets are listed below:

| Domain | Dataset Folder Name | Task ID |
| :--- | :--- | :--- |
| Astronomy | `Astronomy-GWOSC_GW_Event-Gravitational_wave-Anomaly_detection+Event_localisation` | ASU01, ASG02 |
| | `Astronomy-LEAVES-Light_curve-Classification` | ASU03 |
| Earth Science | `Earth_Science-STEAD-Earthquake-Anomaly_detection+Event_localisation` | EAU01, EAG02 |
| Bioacoustics | `Bioacoustics-Powdermill-Birds_vocalisation-Classification` | BIU01 |
| | `Bioacoustics-MarmAudio-Marmoset_vocalisation-Classification` | BIU03 |
| Meteorology | `Meteorology-TS_MQA-Weather-Anomaly_detection` | MEU01 |
| | `Meteorology-TIMECAP-Rainfall-Anomaly_detection` | MEU02 |
| | `Meteorology-MT_bench-Temperature-Forecasting` | MEG03 |
| | `Meteorology-MT_bench-Temperature-MCQ` | MEU04 |
| Economics | `Economics-FinMultiTime-Stock_closing_price-Forecasting` | ECG01 |
| | `Economics-MT_bench-Stock_price-Forecasting` | ECG02 |
| | `Economics-MT_bench-Stock-MCQ` | ECU03 |
| Neuroscience | `Neuroscience-MDD-Depressive_disorder-Anomaly_detection` | NEU01 |
| | `Neuroscience-TUEV-EEG_pattern-Classification` | NEU02 |
| | `Neuroscience-TS_MQA-EEG_signal-Forecasting` | NEG03 |
| | `Neuroscience-TS_MQA-EEG_signal-Imputation` | NEG04 |
| | `Neuroscience-WBCIC_SHU-Motor_imagery-Classification` | NEU05 |
| | `Neuroscience-Sleep-Sleep_staging-Classification` | NEU06 |
| Energy | `Energy-NewsForecast-Electronic_load-Forecasting` | ENG01 |
| | `Energy-TextETT-Sensor_signal_trend-Synthesis` | ENG03 |
| | `Energy-TS_MQA-Comprehensive_electricity-Forecasting` | ENG04 |
| | `Energy-TS_MQA-Comprehensive_electricity-Imputation` | ENG05 |
| Physiology | `Physiology-PTB_XL-ECG_status-Classification` | PHU01 |
| | `Physiology-TS_MQA-Physiological_signal-Forecasting` | PHG02 |
| | `Physiology-TS_MQA-Physiological_signal-Imputation` | PHG03 |
| | `Physiology-TS_MQA-ECG-Anomaly_detection` | PHU04 |
| | `Physiology-TS_MQA-Gait_freezing-Anomaly_detection` | PHU05 |
| | `Physiology-TS_MQA-Human_activity-Classification` | PHU06 |
| Urbanism | `Urbanism-NewsForecast-Traffic_flow-Forecasting` | URG01 |
| | `Urbanism-TS_MQA-Pedestrian_flow-Forecasting` | URG02 |
| | `Urbanism-TS_MQA-Pedestrian_flow-Imputation` | URG03 |
| | `Urbanism-TS_MQA-Traffic_flow-Anomaly_detection` | URU04 |
| | `Urbanism-MetroTraffic-Traffic_volume-Forecasting` | URG05 |
| Manufacturing | `Manufacturing-CWRU-Bearings_fault_location+Bearings_fault_size-Classification` | MFU01, MFU02 |
| | `Manufacturing-MIMII_Due-Machine_malfunction-Anomaly_detection` | MFU03 |
| Radar | `Radar-RadSeg-Coding_scheme-Classification` | RAU01 |
| | `Radar-RadarCom-Modes_and_modulation-Classification` | RAU02 |
| Math | `Math-Chaotic-Chaotic_system-Forecasting` | MAG01 |

## `meta_data.jsonl` Format

Each line in this file is a JSON object with the following structure, providing all necessary metadata to load and use a data sample.

```json
{
    "task_id": ["TASK_ID"], // List of task IDs associated with this sample (e.g., ["ASU03"] or ["ASU01", "ASG02"] for merged datasets)
    "id": "DATASET_ID", // Unique identifier of this sample within the dataset
    "data_type": "csv"/"npy"/"wav"/"flac", // File format of the raw time series data
    "input_ts":{
        "num_channel": int, // Number of channels (dimensions) in the input signal
        "channel_detail": [], // List of channel names, empty if none
        "path": "raw_input_data/sample_001_input.npy",
        "length": int, // Length of the input time series
        "timestamps": [], // Auxiliary timestamp information, empty if none
        "fs": int // Sampling frequency in Hz
    },
    "input_text": "INPUT_TEXT", // Textual prompt or task instruction provided as input
    "gt_text": "GT_TEXT", // Ground truth textual answer (for understanding tasks; empty for generation tasks)
    "gt_ts": {
        "path": "raw_gt_data/sample_001_output.npy",
        "length": int // Length of the ground truth time series
    },
    "gt_result": { ... }, // Structured ground truth result; format varies by task type (see below)
    "meta_data": {}  // Additional metadata from the original data source
}
```

### `gt_result` Field Format

The structure of the `gt_result` field varies depending on the task type. This field provides the original ground truth for metric computation.

**1. MCQ**
```json
"gt_result": {
    "answer": "TEXT" // The correct textual answer
}
```

**2. Synthesis, Forecasting, Imputation**
```json
"gt_result": {
    "num_channel": int, // Number of channels (dimensions) in the ground truth signal
    "channel_detail": [], // List of channel names, empty if none
    "timestamps": [] // Auxiliary timestamp information, empty if none
}
```

**3. Classification**

For the `CWRU` dataset, which involves two classification sub-tasks, the category keys in class_list and gt_class are `"diameter"` and `"position"` respectively. For all other classification tasks, the category key is `"default"`.

```json
"gt_result": {
    "class_list": {
        "default": ["class_A", "class_B"], // List of candidate classes for each category
        ...
    },
    "gt_class": {
        "default": ["GT_CLASS"], // Ground truth class label for each category
        ...
    }
}
```

**4. Anomaly Detection**
```json
"gt_result": {
    "contain": Boolean // Boolean indicating if the required event is present
}
```

**5. Anomaly Detection + Event Localisation**

For the `GWOSC GW Event` and `STEAD` datasets, each of which includes both an `Anomaly Detection` task and an `Event Localisation` task, the gt_result field is defined in the following combined format:

```json
"gt_result": {
    "contain": Boolean, // Boolean indicating if the required event is present
    "start_time": int // The event index if contain is true, else null
}
```

## Handling Restricted Datasets

Due to license restrictions, the **ETT** (`ENG02`) and **iNaturalist** (`BIU02`) datasets are not directly included in this repository. To use them, the user need to download the original data and run the provided processing scripts.

**Step 1: Download the Data**

- **ETT**: Download `ETTh1.csv` from the official repository: [https://github.com/zhouhaoyi/ETDataset](https://github.com/zhouhaoyi/ETDataset)
- **iNaturalist**: Download the `Test Recordings` from the official repository: [https://github.com/visipedia/inat_sounds/tree/main/2024](https://github.com/visipedia/inat_sounds/tree/main/2024)

**Step 2: Install Dependencies**

Before running the processing scripts, install the required Python packages:

```shell
pip install -r process/requirements.txt
```

**Step 3: Run the Processing Script**

Place the downloaded files into a local directory. Then, from the root of this repository, run the corresponding script to process the data into the standard benchmark format.

- For ETT:
  ```shell
  python process/process_ETT.py --data_path /path/to/your/ETTh1.csv
  ```

- For iNaturalist:
  ```shell
  python process/process_iNaturalist.py --data_folder /path/to/your/iNaturalist/test
  ```

This will generate the `Energy-ETT-Transformer_sensor_signal-Forecasting` and `Bioacoustics-INaturalist-Animal_vocalisation-Classification` folders along with their `raw_input_data`, `raw_gt_data` subdirectories, as well as the processed test files.

## Baseline Inference and Evaluation

The `process` directory also includes scripts for running inference and evaluating the results.

### Inference

`process/infer_template.py`: Template code for the inference script. Implement the `initialize_model` function, then inference can be done by running:

```shell
python process/infer_template.py --scits_dir /path/to/scits_dir --output_dir /path/to/output_dir
```

### Evaluation

`process/eval.py`: Evaluation script. Run:

```shell
python process/eval.py evaluate --infer_dir /path/to/infer_dir
```

The evaluation results will be saved to `/path/to/infer_dir/results/`.

## Citation

If you use the SciTS benchmark, please cite the paper:

```bibtex
@inproceedings{
    wu2026scits,
    title={Sci{TS}: {S}cientific Time Series Understanding and Generation with {LLM}s},
    author={Wen Wu and Ziyang Zhang and Liwei Liu and Xuenan Xu and Jimin Zhuang and Ke Fan and Qitan Lv and Junlin Liu and Chen Zhang and Zheqi Yuan and Siyuan Hou and Tianyi Lin and Kai Chen and Bowen Zhou and Chao Zhang},
    booktitle={The Fourteenth International Conference on Learning Representations},
    year={2026},
    url={https://openreview.net/forum?id=5YXccEP6uc}
}
```