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--- |
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license: unknown |
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task_categories: |
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- time-series-forecasting |
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tags: |
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- time-series |
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- forecasting |
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- long-horizon |
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size_categories: |
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- n<1K |
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pretty_name: etth2 |
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--- |
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# etth2 |
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Time series dataset: etth2 |
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## Dataset Description |
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- **Homepage:** |
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- **Repository:** |
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- **Paper:** |
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- **License:** Unknown |
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## Dataset Summary |
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| Property | Value | |
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|----------|-------| |
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| Frequency | 1小时 | |
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| Validation samples | 1 | |
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| Train samples | 1 | |
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| Test samples | 1 | |
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## Supported Tasks |
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- Time series forecasting |
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- Anomaly detection |
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- Classification (if applicable) |
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## Languages |
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N/A (numerical data) |
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## Dataset Structure |
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### Data Instances |
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```json |
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{ |
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"item_id": "example_series_0", |
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"start": "2020-01-01T00:00:00", |
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"target": [1.0, 2.0, 3.0, ...], |
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"frequency": "1H", |
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"metadata": "{...}" |
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} |
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``` |
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### Data Fields |
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| Field | Type | Description | |
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|-------|------|-------------| |
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| item_id | string | Unique identifier for the time series | |
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| start | string | ISO 8601 timestamp of the first observation | |
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| target | list[float] | Time series values | |
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| frequency | string | Pandas frequency string (e.g., '1H', '1D') | |
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| feat_dynamic_real | list[list[float]] | Time-varying covariates (optional) | |
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| feat_static_cat | list[int] | Static categorical features (optional) | |
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| metadata | string | JSON string with normalization params, etc. | |
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### Data Splits |
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| Split | Examples | |
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|-------|----------| |
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| validation | 1 | |
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| train | 1 | |
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| test | 1 | |
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## Dataset Creation |
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### Source Data |
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**Download Method:** unknown |
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### Preprocessing |
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1. Data downloaded from original source |
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2. Missing values filled using forward-fill method |
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3. Standard normalization applied (mean=0, std=1) |
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4. Split into train/validation/test sets (70/10/20) |
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5. Converted to Parquet format for efficient streaming |
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## Considerations for Using the Data |
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### Social Impact |
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This dataset is intended for research purposes in time series forecasting. |
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### Limitations |
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- Normalization parameters are computed on training data only |
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- Missing value handling may introduce artifacts |
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- Temporal alignment assumes regular intervals |
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## Additional Information |
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### Citation |
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```bibtex |
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@misc{unknown_dataset, |
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title = {Unknown Dataset}, |
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url = {}, |
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year = {2024}, |
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} |
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``` |
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### Contributions |
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This dataset was processed and uploaded as part of the [TS Arena](https://github.com/ts-arena) benchmarking project. |
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--- |
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*Generated automatically by TS Arena streaming pipeline on 2026-01-03* |
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