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Datasets:
VGalaxies666
/
m5

Tasks:
Time Series Forecasting
Sub-tasks:
univariate-time-series-forecasting
multivariate-time-series-forecasting
Size:
100K<n<1M
Tags:
time-series
forecasting
benchmark
fev
tsfile
apache-tsfile
License:
Dataset card Files
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metadata 
license: other
task_categories:
  - time-series-forecasting
task_ids:
  - univariate-time-series-forecasting
  - multivariate-time-series-forecasting
annotations_creators:
  - no-annotation
source_datasets:
  - original
tags:
  - time-series
  - forecasting
  - benchmark
  - fev
  - tsfile
  - apache-tsfile
  - modality:timeseries
  - Time-series
  - format:tsfile
  - arxiv:2509.26468
size_categories:
  - 100K<n<1M
pretty_name: m5 (TsFile format)
configs:
  - config_name: default
    data_files:
      - split: train
        path: '**/*.tsfile'

m5 (TsFile format)

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

Summary

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

Dataset Statistics

Frequency Series Median series length TsFile rows (observations) Dynamic columns Static columns Data files
1D 30,490 1,810 428,849,460 9 5 1D/1D_1..1D_46.tsfile (46 shards)
1M 30,490 58 13,805,685 9 5 1M/1M_1..1M_2.tsfile (2 shards)
1W 30,490 257 60,857,703 9 5 1W/1W_1..1W_7.tsfile (7 shards)

Files

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

  • 1D/1D_1.tsfile
  • 1D/1D_10.tsfile
  • 1D/1D_11.tsfile
  • 1D/1D_12.tsfile
  • 1D/1D_13.tsfile
  • 1D/1D_14.tsfile
  • 1D/1D_15.tsfile
  • 1D/1D_16.tsfile
  • 1D/1D_17.tsfile
  • 1D/1D_18.tsfile
  • 1D/1D_19.tsfile
  • 1D/1D_2.tsfile
  • 1D/1D_20.tsfile
  • 1D/1D_21.tsfile
  • 1D/1D_22.tsfile
  • 1D/1D_23.tsfile
  • 1D/1D_24.tsfile
  • 1D/1D_25.tsfile
  • 1D/1D_26.tsfile
  • 1D/1D_27.tsfile
  • ... 35 more .tsfile files

TsFile Storage Model

  • Each original series (id) is stored as one TsFile device.
  • Static covariate columns are stored as TAG columns: item_id, dept_id, cat_id, store_id, state_id.
  • 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): m5_1D, m5_1M, m5_1W.

Column Schema

Column Role TsFile type
Time Time column INT64
id TAG (device dimension) STRING
item_id TAG (device dimension) STRING
dept_id TAG (device dimension) STRING
cat_id TAG (device dimension) STRING
store_id TAG (device dimension) STRING
state_id TAG (device dimension) STRING
target FIELD (measurement) FLOAT
snap_CA FIELD (measurement) BOOLEAN
snap_TX FIELD (measurement) BOOLEAN
snap_WI FIELD (measurement) BOOLEAN
sell_price FIELD (measurement) FLOAT
event_Cultural FIELD (measurement) BOOLEAN
event_National FIELD (measurement) BOOLEAN
event_Religious FIELD (measurement) BOOLEAN
event_Sporting FIELD (measurement) BOOLEAN

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_1.tsfile")
schemas = reader.get_all_table_schemas()
# Table name(s): m5_1D, m5_1M, m5_1W