THULab/favorita_transactions

favorita_transactions (TsFile format)

This repository contains time-series forecasting data stored in Apache TsFile format.

Summary

• —FEV subset: favorita_transactions
• —Unified source collection: `autogluon/fev_datasets`
• —Original source: https://www.kaggle.com/competitions/store-sales-time-series-forecasting
• —Paper / citation: [[8]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation)
• —Series: 51
• —Modalities: Time-series
• —TsFile rows (flattened observations): 288,252
• —Frequencies: 1D, 1M, 1W
• —TsFile files: 3
• —Time precision: milliseconds (INT64).

Licensing and citation requirements follow the original source. This repository does not claim ownership of the original data.

Dataset Statistics

Files

The Hugging Face dataset card YAML points configs.data_files to all *.tsfile files in this repository.

• —1D/1D.tsfile
• —1M/1M.tsfile
• —1W/1W.tsfile

TsFile Storage Model

• —Each original series (id) is stored as one TsFile device.
• —Static covariate columns are stored as TAG columns: store_nbr, city, state, type, cluster.
• —Time-varying targets and dynamic covariates are stored as FIELD measurements.
• —Source timestamp values are mapped to the TsFile Time column as millisecond timestamps.
• —Table name(s): favoritatransactions1D, favoritatransactions1M, favoritatransactions1W.

Column Schema

Note: 153 original id values contained invalid identifier characters and were normalized to valid device names, for example 1→1, 2→2, 3→_3.

Conversion Notes

• —The source FEV format stores each time series as one nested row containing id, timestamp[], and target or covariate arrays.
• —The TsFile conversion flattens those nested arrays into long rows. Therefore, the TsFile rows values above correspond to the number of timestamped observations after flattening.
• —TAG columns identify the device and static metadata. FIELD columns contain values that change over time.
• —Large logical tables may be split into multiple .tsfile shards such as <name>_1.tsfile, <name>_2.tsfile, and so on. Shards listed for the same frequency 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(s): favorita_transactions_1D, favorita_transactions_1M, favorita_transactions_1W