Datasets:

VGalaxies666
/

rideshare

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

rideshare (TsFile format)

156 hourly time series representations of attributes related to Uber and Lyft rideshare services for various locations in New York between 26/11/2018 and 18/12/2018.

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/5122114
Monash subset: rideshare
Modalities: Time-series
Source series: 2,304
Rows: 1,246,464 flattened timestamped observations
Frequency: hourly
Forecast horizon metadata: not specified
Missing-values metadata: True
Equal-length metadata: True
Missing target values preserved as NaN: 494,649
Series length range: 541 to 541
TsFile output: 2 files (rideshare_1.tsfile .. rideshare_2.tsfile)

Files

rideshare_1.tsfile
rideshare_2.tsfile

TsFile Schema

Column	Role	TsFile type
`Time`	TIME	INT64
`series_id`	TAG	STRING
`series_name`	TAG	STRING
`source_location`	TAG	STRING
`provider_name`	TAG	STRING
`provider_service`	TAG	STRING
`type`	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 rideshare.

Reading Example

from tsfile import TsFileReader

reader = TsFileReader("rideshare_1.tsfile")
schemas = reader.get_all_table_schemas()