solar_weekly (TsFile format)

137 time series representing the weekly solar power production in Alabama state in 2006.

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/4656151
• Monash subset: solar_weekly
• Modalities: Time-series
• Source series: 137
• Rows: 7,124 flattened timestamped observations
• Frequency: weekly
• Forecast horizon metadata: 5
• Missing-values metadata: False
• Equal-length metadata: True
• Missing target values preserved as NaN: 0
• Series length range: 52 to 52
• TsFile output: 1 file (solar_weekly.tsfile)

Files

• solar_weekly.tsfile

TsFile Schema

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

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("solar_weekly.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())