Datasets:

VGalaxies666
/

wind_4_seconds

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:
  - time-series
  - forecasting
  - benchmark
  - monash-time-series-forecasting-repository
  - monash-tsf
  - tsfile
  - apache-tsfile
  - modality:timeseries
  - Time-series
  - format:tsfile
pretty_name: wind_4_seconds (TsFile format)
configs:
  - config_name: default
    data_files:
      - split: train
        path: '*.tsfile'

wind_4_seconds (TsFile format)

A single very long daily time series representing the wind power production in MW recorded per every 4 seconds starting from 01/08/2019.

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/4656032
Monash subset: wind_4_seconds
Modalities: Time-series
Source series: 1
Rows: 7,397,147 flattened timestamped observations
Frequency: 4_seconds
Forecast horizon metadata: not specified
Missing-values metadata: False
Equal-length metadata: True
Missing target values preserved as NaN: 0
Series length range: 7,397,147 to 7,397,147
TsFile output: 8 files (wind_4_seconds_1.tsfile .. wind_4_seconds_8.tsfile)

Files

wind_4_seconds_1.tsfile
wind_4_seconds_2.tsfile
wind_4_seconds_3.tsfile
wind_4_seconds_4.tsfile
wind_4_seconds_5.tsfile
wind_4_seconds_6.tsfile
wind_4_seconds_7.tsfile
wind_4_seconds_8.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 wind_4_seconds.

Reading Example

from tsfile import TsFileReader

reader = TsFileReader("wind_4_seconds_1.tsfile")
schemas = reader.get_all_table_schemas()