chore: sync metadata, drop original files

README.md

@@ -1,94 +1,96 @@
----
-license: cc-by-4.0
-annotations_creators:
-- no-annotation
-language_creators:
-- found
-multilinguality:
-- monolingual
-source_datasets:
-- original
-task_categories:
-- time-series-forecasting
-task_ids:
-- univariate-time-series-forecasting
-- multivariate-time-series-forecasting
-tags:
-- forecasting
-- benchmark
-- monash-time-series-forecasting-repository
-- monash-tsf
-- language_creators:found
-- tsfile
-- modality:timeseries
-pretty_name: wind_4_seconds (TsFile format)
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: "*.tsfile"
-modality:
-- timeseries
-size_categories:
-- n<1K
----
-
-# wind_4_seconds (TsFile format)
-
-A single very long daily time series representing the wind power production in MW recorded per every 4 seconds starting from 01/08/2019.
-
-This repository contains the full source `.tsf` series from the Monash Time Series Forecasting Repository converted to [Apache TsFile](https://tsfile.apache.org/) format.
-
-## Summary
-
-- Source dataset: [`Monash-University/monash_tsf`](https://huggingface.co/datasets/Monash-University/monash_tsf)
-- Original source: https://zenodo.org/record/4656032
-- Monash subset: `wind_4_seconds`
-- Modalities: Time-series
-- Source series: 1
-- Rows: 7,397,147 flattened timestamped observations
-- Frequency: `4_seconds`
-- Forecast horizon metadata: not specified
-- Missing-values metadata: False
-- Equal-length metadata: True
-- Missing target values preserved as NaN: 0
-- Series length range: 7,397,147 to 7,397,147
-- TsFile output: 8 files (wind_4_seconds_1.tsfile .. wind_4_seconds_8.tsfile)
-
-## Files
-
-- `wind_4_seconds_1.tsfile`
-- `wind_4_seconds_2.tsfile`
-- `wind_4_seconds_3.tsfile`
-- `wind_4_seconds_4.tsfile`
-- `wind_4_seconds_5.tsfile`
-- `wind_4_seconds_6.tsfile`
-- `wind_4_seconds_7.tsfile`
-- `wind_4_seconds_8.tsfile`
-
-## TsFile Schema
-
-| Column | Role | TsFile type |
-|---|---|---|
-| `Time` | TIME | INT64 |
-| `series_id` | TAG | STRING |
-| `series_name` | TAG | STRING |
-| `start_timestamp` | TAG | STRING |
-| `target` | FIELD | FLOAT |
-
-## Conversion Notes
-
-- Each source `.tsf` data row is stored as one TsFile device.
-- Source `.tsf` attributes are stored as TAG columns.
-- The `target` series values are flattened into timestamped rows and stored as a FLOAT FIELD.
-- `Time` is synthesized from the source start timestamp and the `.tsf` frequency metadata, with millisecond precision.
-- Large outputs may be sharded by the TsFile conversion tool; all listed shards belong to the same logical table `wind_4_seconds`.
-
-## Reading Example
-
-```python
-from tsfile import TsFileReader
-
-reader = TsFileReader("wind_4_seconds_1.tsfile")
-schemas = reader.get_all_table_schemas()
-```
+---
+license: cc-by-4.0
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+multilinguality:
+- monolingual
+source_datasets:
+- original
+task_categories:
+- time-series-forecasting
+task_ids:
+- univariate-time-series-forecasting
+- multivariate-time-series-forecasting
+tags:
+- language_creators:found
+- forecasting
+- benchmark
+- monash-time-series-forecasting-repository
+- monash-tsf
+- tsfile
+- modality:timeseries
+- timeseries
+- format:tsfile
+pretty_name: wind_4_seconds (TsFile format)
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: "*.tsfile"
+modality:
+- timeseries
+size_categories:
+- 1K<n<10K
+---
+
+# wind_4_seconds (TsFile format)
+
+A single very long daily time series representing the wind power production in MW recorded per every 4 seconds starting from 01/08/2019.
+
+This repository contains the full source `.tsf` series from the Monash Time Series Forecasting Repository converted to [Apache TsFile](https://tsfile.apache.org/) format.
+
+## Summary
+
+- Source dataset: [`Monash-University/monash_tsf`](https://huggingface.co/datasets/Monash-University/monash_tsf)
+- Original source: https://zenodo.org/record/4656032
+- Monash subset: `wind_4_seconds`
+- Modalities: Time-series
+- Source series: 1
+- Rows: 7,397,147 flattened timestamped observations
+- Frequency: `4_seconds`
+- Forecast horizon metadata: not specified
+- Missing-values metadata: False
+- Equal-length metadata: True
+- Missing target values preserved as NaN: 0
+- Series length range: 7,397,147 to 7,397,147
+- TsFile output: 8 files (wind_4_seconds_1.tsfile .. wind_4_seconds_8.tsfile)
+
+## Files
+
+- `wind_4_seconds_1.tsfile`
+- `wind_4_seconds_2.tsfile`
+- `wind_4_seconds_3.tsfile`
+- `wind_4_seconds_4.tsfile`
+- `wind_4_seconds_5.tsfile`
+- `wind_4_seconds_6.tsfile`
+- `wind_4_seconds_7.tsfile`
+- `wind_4_seconds_8.tsfile`
+
+## TsFile Schema
+
+| Column | Role | TsFile type |
+|---|---|---|
+| `Time` | TIME | INT64 |
+| `series_id` | TAG | STRING |
+| `series_name` | TAG | STRING |
+| `start_timestamp` | TAG | STRING |
+| `target` | FIELD | FLOAT |
+
+## Conversion Notes
+
+- Each source `.tsf` data row is stored as one TsFile device.
+- Source `.tsf` attributes are stored as TAG columns.
+- The `target` series values are flattened into timestamped rows and stored as a FLOAT FIELD.
+- `Time` is synthesized from the source start timestamp and the `.tsf` frequency metadata, with millisecond precision.
+- Large outputs may be sharded by the TsFile conversion tool; all listed shards belong to the same logical table `wind_4_seconds`.
+
+## Reading Example
+
+```python
+from tsfile import TsFileReader
+
+reader = TsFileReader("wind_4_seconds_1.tsfile")
+schemas = reader.get_all_table_schemas()
+```