| ---
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| license: cc-by-4.0
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| annotations_creators:
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| - no-annotation
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| language_creators:
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| - found
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| multilinguality:
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| - monolingual
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| source_datasets:
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| - original
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| task_categories:
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| - time-series-forecasting
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| task_ids:
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| - univariate-time-series-forecasting
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| tags:
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| - time-series
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| - forecasting
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| - benchmark
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| - monash-time-series-forecasting-repository
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| - monash-tsf
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| - tsfile
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| - apache-tsfile
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| - modality:timeseries
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| - Time-series
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| - format:tsfile
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| pretty_name: temperature_rain (TsFile format)
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| configs:
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| - config_name: default
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| data_files:
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| - split: train
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| path: "*.tsfile"
|
| ---
|
|
|
| # temperature_rain (TsFile format)
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|
|
| 32072 daily time series showing the temperature observations and rain forecasts, gathered by the Australian Bureau of Meteorology for 422 weather stations across Australia, between 02/05/2015 and 26/04/2017
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|
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| This repository contains the full source `.tsf` series from the Monash Time Series Forecasting Repository converted to [Apache TsFile](https://tsfile.apache.org/) format.
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|
|
| ## Summary
|
|
|
| - Source dataset: [`Monash-University/monash_tsf`](https://huggingface.co/datasets/Monash-University/monash_tsf)
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| - Original source: https://zenodo.org/record/5129073
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| - Monash subset: `temperature_rain`
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| - Modalities: Time-series
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| - Source series: 32,072
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| - Rows: 23,252,200 flattened timestamped observations
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| - Frequency: `daily`
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| - Forecast horizon metadata: not specified
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| - Missing-values metadata: True
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| - Equal-length metadata: True
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| - Missing target values preserved as NaN: 598,837
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| - Series length range: 725 to 725
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| - TsFile output: 24 files (temperature_rain_1.tsfile .. temperature_rain_9.tsfile)
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|
|
| ## Files
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|
|
| - `temperature_rain_1.tsfile`
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| - `temperature_rain_10.tsfile`
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| - `temperature_rain_11.tsfile`
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| - `temperature_rain_12.tsfile`
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| - `temperature_rain_13.tsfile`
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| - `temperature_rain_14.tsfile`
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| - `temperature_rain_15.tsfile`
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| - `temperature_rain_16.tsfile`
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| - `temperature_rain_17.tsfile`
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| - `temperature_rain_18.tsfile`
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| - `temperature_rain_19.tsfile`
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| - `temperature_rain_2.tsfile`
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| - `temperature_rain_20.tsfile`
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| - `temperature_rain_21.tsfile`
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| - `temperature_rain_22.tsfile`
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| - `temperature_rain_23.tsfile`
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| - `temperature_rain_24.tsfile`
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| - `temperature_rain_3.tsfile`
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| - `temperature_rain_4.tsfile`
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| - `temperature_rain_5.tsfile`
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| - `temperature_rain_6.tsfile`
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| - `temperature_rain_7.tsfile`
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| - `temperature_rain_8.tsfile`
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| - `temperature_rain_9.tsfile`
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|
|
| ## TsFile Schema
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|
|
| | Column | Role | TsFile type |
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| |---|---|---|
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| | `Time` | TIME | INT64 |
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| | `series_id` | TAG | STRING |
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| | `series_name` | TAG | STRING |
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| | `station_id` | TAG | STRING |
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| | `obs_or_fcst` | TAG | STRING |
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| | `start_timestamp` | TAG | STRING |
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| | `target` | FIELD | FLOAT |
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|
|
| ## Conversion Notes
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|
|
| - Each source `.tsf` data row is stored as one TsFile device.
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| - Source `.tsf` attributes are stored as TAG columns.
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| - The `target` series values are flattened into timestamped rows and stored as a FLOAT FIELD.
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| - `Time` is synthesized from the source start timestamp and the `.tsf` frequency metadata, with millisecond precision.
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| - Large outputs may be sharded by the TsFile conversion tool; all listed shards belong to the same logical table `temperature_rain`.
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|
|
| ## Reading Example
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|
|
| ```python
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| from tsfile import TsFileReader
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|
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| reader = TsFileReader("temperature_rain_1.tsfile")
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| schemas = reader.get_all_table_schemas()
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| ```
|
|
|
