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())