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.

Usage

Install the Apache TsFile Python SDK (pip install tsfile) and read a converted file:

from pathlib import Path
from tsfile import TsFileReader

path = Path("rideshare_1.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    print("tables:", list(schemas))
    table_name = next(iter(schemas))
    table = schemas[table_name]
    print("columns:", [c.get_column_name() for c in table.get_columns()])
    with reader.query_table(table_name, ["target"], batch_size=1024) as result:
        batch = result.read_arrow_batch()
        if batch is not None:
            print(batch.to_pandas().head())