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metadata
license: other
task_categories:
  - time-series-forecasting
tags:
  - time-series
  - tsfile
  - format:tsfile
pretty_name: rossmann (TsFile format)

rossmann (TsFile format)

Source and Citation

This dataset was converted from external source data into a unified forecasting format and then into TsFile. Licensing and citation requirements follow the original source. We do not claim ownership of the original data. Unless otherwise stated, the data is intended for research use.

Dataset Statistics

Frequency Number of series Median length Observations Dynamic columns Static columns Files
1D 1,115 942 7,352,310 7 10 1D/1D_1..1D_2.tsfile (2 shards)
1W 1,115 133 889,770 6 10 1W/1W.tsfile

TsFile Storage Model

  • Each original series (id) is stored as one TsFile device (TAG dimension).
  • Static covariate columns are also stored as TAGs (device metadata): Store, StoreType, Assortment, CompetitionDistance, CompetitionOpenSinceMonth, CompetitionOpenSinceYear, Promo2, Promo2SinceWeek, Promo2SinceYear, PromoInterval.
  • Time-varying targets and dynamic covariates are stored as measurements (FIELDs).
  • timestamp is mapped to Time as INT64 milliseconds.
  • Table names: rossmann_1D, rossmann_1W.

Column Schema

Column Role TsFile type
Time Time column INT64
id TAG (device dimension) STRING
Store TAG (device dimension) DOUBLE
StoreType TAG (device dimension) STRING
Assortment TAG (device dimension) STRING
CompetitionDistance TAG (device dimension) DOUBLE
CompetitionOpenSinceMonth TAG (device dimension) DOUBLE
CompetitionOpenSinceYear TAG (device dimension) DOUBLE
Promo2 TAG (device dimension) DOUBLE
Promo2SinceWeek TAG (device dimension) DOUBLE
Promo2SinceYear TAG (device dimension) DOUBLE
PromoInterval TAG (device dimension) STRING
DayOfWeek FIELD (measurement) FLOAT
Sales FIELD (measurement) FLOAT
Customers FIELD (measurement) FLOAT
Open FIELD (measurement) FLOAT
Promo FIELD (measurement) FLOAT
StateHoliday FIELD (measurement) STRING
SchoolHoliday FIELD (measurement) FLOAT

Note: 2230 original id values contained invalid identifier characters and were normalized to valid device names, for example 1→_1, 2→_2, 3→_3.

Conversion Notes

  • Each original row stores one complete series (id, timestamp[], target and dynamic arrays). The converter flattens these nested rows into a long table before writing TsFile.
  • Numeric types are inferred from the source columns, for example float32 to FLOAT, float64 to DOUBLE, integers to INT64, and bool to BOOLEAN.
  • Time precision: milliseconds (INT64).
  • Large tables may be automatically sharded as <name>_1.tsfile, <name>_2.tsfile, and so on. These shards belong to the same logical table.

Reading Example

from tsfile import TsFileReader

reader = TsFileReader("1D/1D.tsfile")
schemas = reader.get_all_table_schemas()
# Table name: rossmann_1D; see \"Column Schema\" below.