BeTiSe — Benchmark Time Series Generator

A Python library for generating synthetic time series datasets with configurable statistical properties and rich metadata.

Installation

pip install betise

Quick Start

from betise import generate_dataframe, load_config

cfg = load_config(dataset={"base_series": "arma", "num_series": 5, "length_range": [300, 500]})
df, ctx = generate_dataframe(cfg)
print(df[["series_id", "time", "data", "base_components"]].head())

Save to parquet and add feature overlays:

from betise import run, load_config

cfg = load_config(dataset={
    "base_series":  "ar",
    "num_series":   100,
    "length_range": [300, 700],
    "output_dir":   "output",
    "output_name":  "ar_trend.parquet",
    "features": {
        "linear_trend":       {"enabled": True, "direction": "upward"},
        "point_anomaly":      {"enabled": True, "is_spike": True},
    },
})
run(cfg)

Series Types

Feature Overlays

Multiple features can be stacked on top of any base type:

Publication & Dataset

The BeTiSe paper is published in Computer Sciences & Mathematics Forum, 2026, 16(1), Article 24:

BeTiSe: A Comprehensive Benchmark Dataset for Univariate Time Series Stationarity and Structural Analysis

A large-scale benchmark dataset (120,000 series, 23.8 GB) generated with BeTiSe is available on Zenodo:

• Dataset DOI: 10.5281/zenodo.18513505

Documentation & Examples

Full usage guide, config reference, and ready-to-run examples are on GitHub:
github.com/ismailguzel/betise

Citation

Please cite the paper when using BeTiSe:

Gür, K., Yazıcı, P., Erkaya, P., Türkmen, Y., Baytak, B., Güzel, İ., Karagöz, P., & Yozgatlıgil, C. (2026). BeTiSe: A comprehensive benchmark dataset for univariate time series stationarity and structural analysis. Computer Sciences & Mathematics Forum, 16(1), Article 24. https://doi.org/10.3390/cmsf2026016024

@article{gur2026betise,
  author  = {Gür, Kerem R and Yazıcı, P. Cemre and Erkaya, Pelin and Türkmen, Yağmur and Baytak, Berke and Güzel, İsmail and Karagöz, Pınar and Yozgatlıgil, Ceylan},
  title   = {{BeTiSe}: A Comprehensive Benchmark Dataset for Univariate Time Series Stationarity and Structural Analysis},
  journal = {Computer Sciences & Mathematics Forum},
  year    = {2026},
  volume  = {16},
  number  = {1},
  pages   = {24},
  doi     = {10.3390/cmsf2026016024},
  url     = {https://doi.org/10.3390/cmsf2026016024}
}

If you use the published benchmark dataset, please also cite its Zenodo record:

@dataset{betise2026,
  author    = {Gür, Kerem R and Yazıcı, P. Cemre and Erkaya, Pelin and Türkmen, Yağmur and Baytak, Berke and Güzel, İsmail and Karagöz, Pınar and Yozgatlıgil, Ceylan},
  title     = {{BeTiSe: A Benchmark Time Series Dataset for Stationarity
                and Structural Analysis}},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.18513505},
  url       = {https://doi.org/10.5281/zenodo.18513505}
}

Contact

İsmail Güzel — ismailgzel@gmail.com

License

MIT — see LICENSE.