README.md

metadata

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
  - multivariate-time-series-forecasting
tags:
  - language_creators:found
  - forecasting
  - benchmark
  - monash-time-series-forecasting-repository
  - monash-tsf
  - tsfile
  - modality:timeseries
  - timeseries
  - format:tsfile
pretty_name: car_parts (TsFile format)
configs:
  - config_name: default
    data_files:
      - split: train
        path: '*.tsfile'
modality:
  - timeseries
size_categories:
  - 1K<n<10K

car_parts (TsFile format)

2674 intermittent monthly time series that represent car parts sales from January 1998 to March 2002.

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/4656022
• Monash subset: car_parts
• Modalities: Time-series
• Source series: 2,674
• Rows: 136,374 flattened timestamped observations
• Frequency: monthly
• Forecast horizon metadata: not specified
• Missing-values metadata: True
• Equal-length metadata: True
• Missing target values preserved as NaN: 6,122
• Series length range: 51 to 51
• TsFile output: 1 file (car_parts.tsfile)

Files

• car_parts.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 car_parts.

Reading Example

from tsfile import TsFileReader

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