Datasets:

VGalaxies666
/

cif_2016

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: cif_2016 (TsFile format)
configs:
  - config_name: default
    data_files:
      - split: train
        path: '*.tsfile'

cif_2016 (TsFile format)

72 monthly time series originated from the banking domain used in the CIF 2016 forecasting competition.

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/4656042
Monash subset: cif_2016
Modalities: Time-series
Source series: 72
Rows: 7,108 flattened timestamped observations
Frequency: monthly
Forecast horizon metadata: not specified
Missing-values metadata: False
Equal-length metadata: False
Missing target values preserved as NaN: 0
Series length range: 28 to 120
TsFile output: 1 file (cif_2016.tsfile)

Files

cif_2016.tsfile

TsFile Schema

Column	Role	TsFile type
`Time`	TIME	INT64
`series_id`	TAG	STRING
`series_name`	TAG	STRING
`horizon`	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 cif_2016.

Reading Example

from tsfile import TsFileReader

reader = TsFileReader("cif_2016.tsfile")
schemas = reader.get_all_table_schemas()