epf_fr/README.md

metadata

license: other
task_categories:
  - time-series-forecasting
tags:
  - time-series
  - tsfile
  - format:tsfile
pretty_name: epf_fr (TsFile format)

epf_fr (TsFile format)

Source and Citation

• Original source: https://zenodo.org/records/4624805
• Paper / citation: [7]
• Unified source collection: autogluon/fev_datasets

This dataset was converted from external source data into a unified forecasting format and then into TsFile. Licensing and citation requirements follow the original source. We do not claim ownership of the original data. Unless otherwise stated, the data is intended for research use.

Dataset Statistics

Number of series	Median length	Observations	Dynamic columns	Static columns	Files
1	52,416	157,248	3	0	epf_fr.tsfile

TsFile Storage Model

• Each original series (id) is stored as one TsFile device (TAG dimension).
• Time-varying targets and dynamic covariates are stored as measurements (FIELDs).
• timestamp is mapped to Time as INT64 milliseconds.
• Table name: epf_fr.

Column Schema

Column	Role	TsFile type
Time	Time column	INT64
id	TAG (device dimension)	STRING
target	FIELD (measurement)	FLOAT
Generation_forecast	FIELD (measurement)	FLOAT
System_load_forecast	FIELD (measurement)	FLOAT

Conversion Notes

• Each original row stores one complete series (id, timestamp[], target and dynamic arrays). The converter flattens these nested rows into a long table before writing TsFile.
• Numeric types are inferred from the source columns, for example float32 to FLOAT, float64 to DOUBLE, integers to INT64, and bool to BOOLEAN.
• Time precision: milliseconds (INT64).
• Large tables may be automatically sharded as <name>_1.tsfile, <name>_2.tsfile, and so on. These shards belong to the same logical table.

Reading Example

from tsfile import TsFileReader

reader = TsFileReader("epf_fr.tsfile")
schemas = reader.get_all_table_schemas()
# Table name: epf_fr; see \"Column Schema\" below.