Datasets:

THULab
/

superstore_data

pretty_name: Superstore Sales POS (TsFile)
modality: timeseries
authors: An-j96
task_categories:
  - time-series-forecasting
size_categories:
  - 1K<n<10K
tags:
  - tsfile
  - timeseries
  - modality:timeseries
  - format:tsfile
  - finance
configs:
  - config_name: default
    data_files:
      - split: train
        path: superstore_data.tsfile

Superstore Sales POS (TsFile)

This dataset is an Apache TsFile conversion of An-j96/SuperstoreData.

Modalities: Time-series.

Overview

Superstore point-of-sale transactions for sales/demographics forecasting.
Each order records segment, category, region, ship mode, and sale metrics (Sales, Quantity, Discount, Profit).
Order dimensions are device TAGs; sales metrics are FIELDs.
Converted observations: 9,994 rows across 1 TsFile file(s)
Source format: csv

TsFile schema

Time — source Order Date (%m/%d/%Y), converted to INT64 milliseconds.

Column	Role	Type	Meaning
`Time`	TIME	INT64 (ms)	sample timestamp
`Row_ID`	TAG	STRING	order id
`Segment`	TAG	STRING	customer segment
`Category`	TAG	STRING	product category
`Region`	TAG	STRING	region
`Ship_Mode`	TAG	STRING	shipping mode
`Sales`	FIELD	FLOAT	sales amount
`Quantity`	FIELD	FLOAT	quantity
`Discount`	FIELD	FLOAT	discount
`Profit`	FIELD	FLOAT	profit

Conversion notes

Row_ID, Segment, Category, Region, Ship_Mode kept as TAGs; Sales/Quantity/Discount/Profit as FLOAT/INT64 FIELDs.

Source & license

Original dataset: https://huggingface.co/datasets/An-j96/SuperstoreData
Author / publisher: An-j96
License: gpl-2.0

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("superstore_data.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]
    columns = [column.get_column_name() for column in table.get_columns()]
    print("columns:", columns)
    field_names = [
        column.get_column_name()
        for column in table.get_columns()
        if column.get_column_name() not in {"Time", "time"}
    ]
    if field_names:
        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
            batch = result.read_arrow_batch()
            if batch is not None:
                print(batch.to_pandas().head())