---
license: unknown
task_categories:
  - time-series-forecasting
tags:
  - time-series
  - forecasting
  - long-horizon
size_categories:
  - n<1K
pretty_name: etth2
---

# etth2

Time series dataset: etth2

## Dataset Description

- **Homepage:** 
- **Repository:** 
- **Paper:** 
- **License:** Unknown

## Dataset Summary

| Property | Value |
|----------|-------|
| Frequency | 1小时 |
| Validation samples | 1 |
| Train samples | 1 |
| Test samples | 1 |

## Supported Tasks

- Time series forecasting
- Anomaly detection
- Classification (if applicable)

## Languages

N/A (numerical data)

## Dataset Structure

### Data Instances

```json
{
    "item_id": "example_series_0",
    "start": "2020-01-01T00:00:00",
    "target": [1.0, 2.0, 3.0, ...],
    "frequency": "1H",
    "metadata": "{...}"
}
```

### Data Fields

| Field | Type | Description |
|-------|------|-------------|
| item_id | string | Unique identifier for the time series |
| start | string | ISO 8601 timestamp of the first observation |
| target | list[float] | Time series values |
| frequency | string | Pandas frequency string (e.g., '1H', '1D') |
| feat_dynamic_real | list[list[float]] | Time-varying covariates (optional) |
| feat_static_cat | list[int] | Static categorical features (optional) |
| metadata | string | JSON string with normalization params, etc. |

### Data Splits

| Split | Examples |
|-------|----------|
| validation | 1 |
| train | 1 |
| test | 1 |

## Dataset Creation

### Source Data

**Download Method:** unknown

### Preprocessing

1. Data downloaded from original source
2. Missing values filled using forward-fill method
3. Standard normalization applied (mean=0, std=1)
4. Split into train/validation/test sets (70/10/20)
5. Converted to Parquet format for efficient streaming

## Considerations for Using the Data

### Social Impact

This dataset is intended for research purposes in time series forecasting.

### Limitations

- Normalization parameters are computed on training data only
- Missing value handling may introduce artifacts
- Temporal alignment assumes regular intervals

## Additional Information

### Citation

```bibtex
@misc{unknown_dataset,
    title = {Unknown Dataset},
    url = {},
    year = {2024},
}
```

### Contributions

This dataset was processed and uploaded as part of the [TS Arena](https://github.com/ts-arena) benchmarking project.

---
*Generated automatically by TS Arena streaming pipeline on 2026-01-03*