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Datasets:
VGalaxies666
/
boomlet

Tasks:
Time Series Forecasting
Modalities:
Time-series
Sub-tasks:
univariate-time-series-forecasting
multivariate-time-series-forecasting
Size:
n<1K
ArXiv:
Tags:
forecasting
benchmark
fev
arxiv:2509.26468
tsfile
modality:timeseries
License:
Dataset card Files
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boomlet / README.md
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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:
  - forecasting
  - benchmark
  - fev
  - arxiv:2509.26468
  - tsfile
  - modality:timeseries
  - timeseries
  - format:tsfile
size_categories:
  - n<1K
pretty_name: boomlet (TsFile format)
configs:
  - config_name: default
    data_files:
      - split: train
        path: '**/*.tsfile'
modality:
  - timeseries

boomlet (TsFile format)

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

Summary

  • FEV subset: boomlet
  • Unified source collection: autogluon/fev_datasets
  • Original source: https://huggingface.co/datasets/Datadog/BOOM
  • Paper / citation: [5]
  • Series: 1
  • Modalities: Time-series
  • TsFile rows (flattened observations): 10,139,745
  • Frequencies: 1062, 1209, 1225, 1230, 1282, 1487, 1631, 1676, 1855, 1975, 2187, 285, 619, 772, 963
  • TsFile files: 15
  • Time precision: milliseconds (INT64).

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
1062 1 16,384 344,064 21 6 1062/1062.tsfile
1209 1 16,384 868,352 53 6 1209/1209.tsfile
1225 1 16,384 802,816 49 6 1225/1225.tsfile
1230 1 16,384 376,832 23 6 1230/1230.tsfile
1282 1 16,384 573,440 35 6 1282/1282.tsfile
1487 1 16,384 884,736 54 6 1487/1487.tsfile
1631 1 10,463 418,520 40 6 1631/1631.tsfile
1676 1 10,463 1,046,300 100 6 1676/1676.tsfile
1855 1 5,231 272,012 52 6 1855/1855.tsfile
1975 1 5,231 392,325 75 6 1975/1975.tsfile
2187 1 5,231 523,100 100 6 2187/2187.tsfile
285 1 16,384 1,228,800 75 6 285/285.tsfile
619 1 16,384 851,968 52 6 619/619.tsfile
772 1 16,384 1,097,728 67 6 772/772.tsfile
963 1 16,384 458,752 28 6 963/963.tsfile

Files

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

  • 1062/1062.tsfile
  • 1209/1209.tsfile
  • 1225/1225.tsfile
  • 1230/1230.tsfile
  • 1282/1282.tsfile
  • 1487/1487.tsfile
  • 1631/1631.tsfile
  • 1676/1676.tsfile
  • 1855/1855.tsfile
  • 1975/1975.tsfile
  • 2187/2187.tsfile
  • 285/285.tsfile
  • 619/619.tsfile
  • 772/772.tsfile
  • 963/963.tsfile

TsFile Storage Model

  • Each original series (id) is stored as one TsFile device.
  • Static covariate columns are stored as TAG columns: type, Application_Usage, Infrastructure, Database, Networking, Security.
  • 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): boomlet_1062, boomlet_1209, boomlet_1225, boomlet_1230, boomlet_1282, boomlet_1487, boomlet_1631, boomlet_1676, boomlet_1855, boomlet_1975, boomlet_2187, boomlet_285, boomlet_619, boomlet_772, boomlet_963.

Column Schema

Column Role TsFile type
Time Time column INT64
id TAG (device dimension) STRING
type TAG (device dimension) STRING
Application_Usage TAG (device dimension) DOUBLE
Infrastructure TAG (device dimension) DOUBLE
Database TAG (device dimension) DOUBLE
Networking TAG (device dimension) DOUBLE
Security TAG (device dimension) DOUBLE
target_0 FIELD (measurement) FLOAT
target_1 FIELD (measurement) FLOAT
target_2 FIELD (measurement) FLOAT
target_3 FIELD (measurement) FLOAT
target_4 FIELD (measurement) FLOAT
target_5 FIELD (measurement) FLOAT
target_6 FIELD (measurement) FLOAT
target_7 FIELD (measurement) FLOAT
target_8 FIELD (measurement) FLOAT
target_9 FIELD (measurement) FLOAT
target_10 FIELD (measurement) FLOAT
target_11 FIELD (measurement) FLOAT
target_12 FIELD (measurement) FLOAT
target_13 FIELD (measurement) FLOAT
target_14 FIELD (measurement) FLOAT
target_15 FIELD (measurement) FLOAT
target_16 FIELD (measurement) FLOAT
target_17 FIELD (measurement) FLOAT
target_18 FIELD (measurement) FLOAT
target_19 FIELD (measurement) FLOAT
target_20 FIELD (measurement) FLOAT

Note: 15 original id values contained invalid identifier characters and were normalized to valid device names, for example 1062→_1062, 1209→_1209, 1225→_1225.

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("1062/1062.tsfile")
schemas = reader.get_all_table_schemas()
# Table name(s): boomlet_1062, boomlet_1209, boomlet_1225, boomlet_1230, boomlet_1282, boomlet_1487, boomlet_1631, boomlet_1676, boomlet_1855, boomlet_1975, boomlet_2187, boomlet_285, boomlet_619, boomlet_772, boomlet_963