Datasets:

wangdx25
/

temperature_rain

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 format.

Summary

Source dataset: 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:

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())