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