Datasets:

VGalaxies666
/

nn5_weekly

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: nn5_weekly (TsFile format)
configs:
  - config_name: default
    data_files:
      - split: train
        path: '*.tsfile'

nn5_weekly (TsFile format)

111 time series to predicting the weekly cash withdrawals from ATMs in UK.

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/4656125
Monash subset: nn5_weekly
Modalities: Time-series
Source series: 111
Rows: 12,543 flattened timestamped observations
Frequency: weekly
Forecast horizon metadata: 8
Missing-values metadata: False
Equal-length metadata: True
Missing target values preserved as NaN: 0
Series length range: 113 to 113
TsFile output: 1 file (nn5_weekly.tsfile)

Files

nn5_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 nn5_weekly.

Reading Example

from tsfile import TsFileReader

reader = TsFileReader("nn5_weekly.tsfile")
schemas = reader.get_all_table_schemas()