Add files using upload-large-folder tool

README.md

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+---
+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
+tags:
+- time-series
+- forecasting
+- benchmark
+- monash-time-series-forecasting-repository
+- monash-tsf
+- tsfile
+- apache-tsfile
+- modality:timeseries
+- Time-series
+- format:tsfile
+pretty_name: nn5_weekly (TsFile format)
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: "*.tsfile"
+---
+
+# nn5_weekly (TsFile format)
+
+111 time series to predicting the weekly cash withdrawals from ATMs in UK.
+
+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/4656125
+- Monash subset: `nn5_weekly`
+- Modalities: Time-series
+- Source series: 111
+- Rows: 12,543 flattened timestamped observations
+- Frequency: `weekly`
+- Forecast horizon metadata: 8
+- Missing-values metadata: False
+- Equal-length metadata: True
+- Missing target values preserved as NaN: 0
+- Series length range: 113 to 113
+- TsFile output: 1 file (nn5_weekly.tsfile)
+
+## Files
+
+- `nn5_weekly.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 `nn5_weekly`.
+
+## Reading Example
+
+```python
+from tsfile import TsFileReader
+
+reader = TsFileReader("nn5_weekly.tsfile")
+schemas = reader.get_all_table_schemas()
+```