weather (TsFile format)

3010 daily time series representing the variations of four weather variables: rain, mintemp, maxtemp and solar radiation, measured at the weather stations in Australia.

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/4654822
• Monash subset: weather
• Modalities: Time-series
• Source series: 3,010
• Rows: 43,032,000 flattened timestamped observations
• Frequency: daily
• Forecast horizon metadata: not specified
• Missing-values metadata: False
• Equal-length metadata: False
• Missing target values preserved as NaN: 0
• Series length range: 1,332 to 65,981
• TsFile output: 43 files (weather_1.tsfile .. weather_9.tsfile)

Files

• weather_1.tsfile
• weather_10.tsfile
• weather_11.tsfile
• weather_12.tsfile
• weather_13.tsfile
• weather_14.tsfile
• weather_15.tsfile
• weather_16.tsfile
• weather_17.tsfile
• weather_18.tsfile
• weather_19.tsfile
• weather_2.tsfile
• weather_20.tsfile
• weather_21.tsfile
• weather_22.tsfile
• weather_23.tsfile
• weather_24.tsfile
• weather_25.tsfile
• weather_26.tsfile
• weather_27.tsfile
• weather_28.tsfile
• weather_29.tsfile
• weather_3.tsfile
• weather_30.tsfile
• weather_31.tsfile
• weather_32.tsfile
• weather_33.tsfile
• weather_34.tsfile
• weather_35.tsfile
• weather_36.tsfile
• ... 13 more .tsfile shards

TsFile Schema

Column	Role	TsFile type
Time	TIME	INT64
series_id	TAG	STRING
series_name	TAG	STRING
series_type	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 weather.

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("weather_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())