Add files using upload-large-folder tool

.gitattributes

@@ -58,3 +58,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+rideshare_1.tsfile filter=lfs diff=lfs merge=lfs -text
+rideshare_2.tsfile filter=lfs diff=lfs merge=lfs -text

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: rideshare (TsFile format)
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: "*.tsfile"
+---
+
+# rideshare (TsFile format)
+
+156 hourly time series representations of attributes related to Uber and Lyft rideshare services for various locations in New York between 26/11/2018 and 18/12/2018.
+
+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/5122114
+- Monash subset: `rideshare`
+- Modalities: Time-series
+- Source series: 2,304
+- Rows: 1,246,464 flattened timestamped observations
+- Frequency: `hourly`
+- Forecast horizon metadata: not specified
+- Missing-values metadata: True
+- Equal-length metadata: True
+- Missing target values preserved as NaN: 494,649
+- Series length range: 541 to 541
+- TsFile output: 2 files (rideshare_1.tsfile .. rideshare_2.tsfile)
+
+## Files
+
+- `rideshare_1.tsfile`
+- `rideshare_2.tsfile`
+
+## TsFile Schema
+
+| Column | Role | TsFile type |
+|---|---|---|
+| `Time` | TIME | INT64 |
+| `series_id` | TAG | STRING |
+| `series_name` | TAG | STRING |
+| `source_location` | TAG | STRING |
+| `provider_name` | TAG | STRING |
+| `provider_service` | TAG | STRING |
+| `type` | 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 `rideshare`.
+
+## Reading Example
+
+```python
+from tsfile import TsFileReader
+
+reader = TsFileReader("rideshare_1.tsfile")
+schemas = reader.get_all_table_schemas()
+```

rideshare_1.tsfile

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+oid sha256:dd431ba04d1ca828d75ba8594b97519cfbcccf71afb03d0203c483ad249910f2
+size 2157436

rideshare_2.tsfile

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4badf0045e66136eed64010e665bb66e9b92eb9fd1c83474c73fb7b499f1da3c
+size 525828