---
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:
- tsfile
- format:tsfile
- timeseries
- time-series
pretty_name: temperature_rain (TsFile format)
configs:
- config_name: default
  data_files:
  - split: train
    path: "*.tsfile"
---

# temperature_rain (TsFile format)

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

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/5129073
- Monash subset: `temperature_rain`
- Modalities: Time-series
- Source series: 32,072
- Rows: 23,252,200 flattened timestamped observations
- Frequency: `daily`
- Forecast horizon metadata: not specified
- Missing-values metadata: True
- Equal-length metadata: True
- Missing target values preserved as NaN: 598,837
- Series length range: 725 to 725
- TsFile output: 24 files (temperature_rain_1.tsfile .. temperature_rain_9.tsfile)

## Files

- `temperature_rain_1.tsfile`
- `temperature_rain_10.tsfile`
- `temperature_rain_11.tsfile`
- `temperature_rain_12.tsfile`
- `temperature_rain_13.tsfile`
- `temperature_rain_14.tsfile`
- `temperature_rain_15.tsfile`
- `temperature_rain_16.tsfile`
- `temperature_rain_17.tsfile`
- `temperature_rain_18.tsfile`
- `temperature_rain_19.tsfile`
- `temperature_rain_2.tsfile`
- `temperature_rain_20.tsfile`
- `temperature_rain_21.tsfile`
- `temperature_rain_22.tsfile`
- `temperature_rain_23.tsfile`
- `temperature_rain_24.tsfile`
- `temperature_rain_3.tsfile`
- `temperature_rain_4.tsfile`
- `temperature_rain_5.tsfile`
- `temperature_rain_6.tsfile`
- `temperature_rain_7.tsfile`
- `temperature_rain_8.tsfile`
- `temperature_rain_9.tsfile`

## TsFile Schema

| Column | Role | TsFile type |
|---|---|---|
| `Time` | TIME | INT64 |
| `series_id` | TAG | STRING |
| `series_name` | TAG | STRING |
| `station_id` | TAG | STRING |
| `obs_or_fcst` | 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 `temperature_rain`.

## Usage

Install the Apache TsFile Python SDK (`pip install tsfile`) and read a
converted file:

```python
from pathlib import Path
from tsfile import TsFileReader

path = Path("temperature_rain_1.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    print("tables:", list(schemas))
    table_name = next(iter(schemas))
    table = schemas[table_name]
    print("columns:", [c.get_column_name() for c in table.get_columns()])
    with reader.query_table(table_name, ["target"], batch_size=1024) as result:
        batch = result.read_arrow_batch()
        if batch is not None:
            print(batch.to_pandas().head())
```