Rewrite dataset card in English

README.md

@@ -2,34 +2,71 @@
 license: other
 task_categories:
 - time-series-forecasting
+task_ids:
+- univariate-time-series-forecasting
+- multivariate-time-series-forecasting
+annotations_creators:
+- no-annotation
+source_datasets:
+- original
 tags:
 - time-series
+- forecasting
+- benchmark
+- fev
 - tsfile
+- apache-tsfile
+- modality:timeseries
+- Time-series
+- format:tsfile
+- arxiv:2509.26468
+size_categories:
+- 100K<n<1M
 pretty_name: epf_de (TsFile format)
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: "**/*.tsfile"
 ---
 
 # epf_de (TsFile format)
 
-## Source and Citation
+This repository contains time-series forecasting data stored in [Apache TsFile](https://tsfile.apache.org/) format.
 
-- **Original source**: https://zenodo.org/records/4624805
-- **Paper / citation**: [[7]](https://doi.org/10.1016/j.apenergy.2021.116983)
-- **Unified source collection**: [autogluon/fev_datasets](https://huggingface.co/datasets/autogluon/fev_datasets)
+## Summary
 
-> This dataset was converted from external source data into a unified forecasting format and then into TsFile. Licensing and citation requirements follow the original source. We do not claim ownership of the original data. Unless otherwise stated, the data is intended for research use.
+- FEV subset: `epf_de`
+- Unified source collection: [`autogluon/fev_datasets`](https://huggingface.co/datasets/autogluon/fev_datasets)
+- Original source: https://zenodo.org/records/4624805
+- Paper / citation: [[7]](https://doi.org/10.1016/j.apenergy.2021.116983)
+- Series: 1
+- Modalities: Time-series
+- TsFile rows (flattened observations): 157,248
+- Frequencies: source-defined
+- TsFile files: 1
+- Time precision: milliseconds (`INT64`).
+
+Licensing and citation requirements follow the original source. This repository does not claim ownership of the original data.
 
 ## Dataset Statistics
 
-| Number of series | Median length | Observations | Dynamic columns | Static columns | Files |
-|---|---|---|---|---|---|
+| Series | Median series length | TsFile rows (observations) | Dynamic columns | Static columns | Data files |
+|---:|---:|---:|---:|---:|---|
 | 1 | 52,416 | 157,248 | 3 | 0 | `epf_de.tsfile` |
 
+## Files
+
+The Hugging Face dataset card YAML points `configs.data_files` to all `*.tsfile` files in this repository.
+
+- `epf_de.tsfile`
+
 ## TsFile Storage Model
 
-- Each original series (`id`) is stored as one TsFile **device** (TAG dimension).
-- Time-varying targets and dynamic covariates are stored as **measurements** (FIELDs).
-- `timestamp` is mapped to `Time` as INT64 milliseconds.
-- Table name: epf_de.
+- Each original series (`id`) is stored as one TsFile device.
+- Time-varying targets and dynamic covariates are stored as FIELD measurements.
+- Source `timestamp` values are mapped to the TsFile `Time` column as millisecond timestamps.
+- Table name(s): epf_de.
 
 ### Column Schema
 
@@ -43,10 +80,10 @@ pretty_name: epf_de (TsFile format)
 
 ## Conversion Notes
 
-- Each original row stores one complete series `(id, timestamp[], target and dynamic arrays)`. The converter flattens these nested rows into a long table before writing TsFile.
-- Numeric types are inferred from the source columns, for example float32 to FLOAT, float64 to DOUBLE, integers to INT64, and bool to BOOLEAN.
-- Time precision: milliseconds (INT64).
-- Large tables may be automatically sharded as `<name>_1.tsfile`, `<name>_2.tsfile`, and so on. These shards belong to the same logical table.
+- The source FEV format stores each time series as one nested row containing `id`, `timestamp[]`, and target or covariate arrays.
+- The TsFile conversion flattens those nested arrays into long rows. Therefore, the `TsFile rows` values above correspond to the number of timestamped observations after flattening.
+- TAG columns identify the device and static metadata. FIELD columns contain values that change over time.
+- Large logical tables may be split into multiple `.tsfile` shards such as `<name>_1.tsfile`, `<name>_2.tsfile`, and so on. Shards listed for the same frequency belong to the same logical table.
 
 ## Reading Example
 
@@ -55,5 +92,5 @@ from tsfile import TsFileReader
 
 reader = TsFileReader("epf_de.tsfile")
 schemas = reader.get_all_table_schemas()
-# Table name: epf_de; see \"Column Schema\" below.
+# Table name(s): epf_de
 ```