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README.md

@@ -1,54 +1,121 @@
 ---
-license: mit
+license: unknown
 task_categories:
-- time-series-forecasting
+  - time-series-forecasting
 tags:
-- time-series
-- forecasting
-- ts-arena
-- long-horizon
+  - time-series
+  - forecasting
+  - long-horizon
 size_categories:
-- 10K<n<100K
+  - n<1K
+pretty_name: electricity
 ---
 
-# Electricity Dataset
+# electricity
 
-Part of the TS Arena benchmarking suite for time-series foundation models.
+Time series dataset: electricity
 
-## Description
+## Dataset Description
 
-321 customers electricity consumption (2012-2014)
+- **Homepage:** 
+- **Repository:** 
+- **Paper:** 
+- **License:** Unknown
 
-## Statistics
+## Dataset Summary
 
-- **Total samples**: 26304
-- **Features**: 321
-- **Train samples**: 18412
-- **Validation samples**: 2631
-- **Test samples**: 5261
-- **Frequency**: 1 hour
+| Property | Value |
+|----------|-------|
+| Frequency | 1H |
+| Validation samples | 1 |
+| Train samples | 1 |
+| Test samples | 1 |
 
-## Preprocessing
+## Supported Tasks
 
-- Standard normalization (zero mean, unit variance)
-- Scaler fitted on training data only
-- Train/Val/Test split: 70%/10%/20%
+- Time series forecasting
+- Anomaly detection
+- Classification (if applicable)
 
-## Usage
+## Languages
 
-```python
-from datasets import load_dataset
+N/A (numerical data)
 
-# Load a specific split
-train = load_dataset("ts-arena/electricity", split="train")
-val = load_dataset("ts-arena/electricity", split="validation")
-test = load_dataset("ts-arena/electricity", split="test")
+## 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": "{...}"
+}
 ```
 
-## Source
+### 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
 
-[Autoformer Dataset Collection](https://github.com/thuml/Autoformer)
+## Considerations for Using the Data
 
-## License
+### Social Impact
 
-MIT
+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*