tsdb
TSDB (Time Series Data Beans): a Python toolbox helping load 172 open-source time-series datasets
Decision gist · record as of 2026-08-14
Yes. TSDB is actively maintained, has no known vulnerabilities, low install friction, and solves a real friction point—downloading and organizing public time-series datasets. It is well-suited for researchers and engineers working on time-series tasks who want to avoid manual dataset management. The permissive BSD license poses no barrier. Install it if you regularly work with benchmark time-series data.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.8 or later; first load of a dataset downloads and caches it locally (default ~/.pypots/tsdb).
- Low install friction with a pure-Python wheel and seven common dependencies (numpy, scipy, pandas, pyarrow, scikit-learn, requests, tqdm).
- Active maintenance with recent commits and production-stable status.
License · maintenance · safety
permissive license (permissive) — BSD 3-Clause permissive license allows commercial and private use with minimal restrictions; you must retain copyright notices and disclaimers in source distributions.
last release 2026-04-09 (127 days) · last repo commit 2026-08-06 · 239 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 127,808 downloads/mo, #11,730 on PyPI
Alternatives
Verify before relying
pip install tsdb
import tsdb
data = tsdb.load('physionet_2012')
tsdb.list() # see all 173 available datasets- Whether all 173 datasets remain consistently available and whether download reliability is documented.
- Performance characteristics when loading large datasets or working with many cached datasets.
- Whether data preprocessing beyond 'removing invalid samples' is actually performed or if raw download is truly the only option.
What it is and what it does
TSDB is a dataset management library that centralizes access to 173 public time-series datasets commonly used in research and benchmarking. It handles downloading, extraction, and caching automatically, letting you load datasets with a single function call. The library is designed to decouple dataset management from machine learning algorithms, so you can experiment with different datasets without writing custom download and preprocessing code.
The package performs minimal data processing—mainly removing invalid samples—and provides both processed and raw dataset access. It manages a local cache (by default in ~/.pypots/tsdb) and lets you list available datasets, delete specific cached items, or migrate the cache directory to external storage. Dependencies are standard data-science libraries (numpy, scipy, pandas, scikit-learn, requests, tqdm), making it straightforward to integrate into existing workflows.
Use it for
- Benchmark time-series forecasting models against standard datasets like PhysioNet or PeMS traffic data without manual downloading.
- Rapidly prototype imputation and classification algorithms on diverse datasets (air quality, electricity, vessel AIS) with one-line loading.
- Access all 163 UCR & UEA classification datasets for time-series classification research without managing individual downloads.
- Manage dataset caching and storage on resource-constrained machines by migrating the cache directory to external disks.
- Build reproducible research pipelines where dataset versions and availability are centrally maintained by the TSDB project.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
TSDB is actively maintained, has no known vulnerabilities, low install friction, and solves a real friction point—downloading and organizing public time-series datasets. It is well-suited for researchers and engineers working on time-series tasks who want to avoid manual dataset management. The permissive BSD license poses no barrier. Install it if you regularly work with benchmark time-series data.
Install
tsdb on PyPI
Before you install
Low install friction with a pure-Python wheel and seven common dependencies (numpy, scipy, pandas, pyarrow, scikit-learn, requests, tqdm). Active maintenance with recent commits and production-stable status.
Requires Python 3.8 or later; first load of a dataset downloads and caches it locally (default ~/.pypots/tsdb).
License in practice
BSD 3-Clause permissive license allows commercial and private use with minimal restrictions; you must retain copyright notices and disclaimers in source distributions.
Quickstart
pip install tsdb
import tsdb
data = tsdb.load('physionet_2012')
tsdb.list() # see all 173 available datasetsVerify before relying
- Whether all 173 datasets remain consistently available and whether download reliability is documented.
- Performance characteristics when loading large datasets or working with many cached datasets.
- Whether data preprocessing beyond 'removing invalid samples' is actually performed or if raw download is truly the only option.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagestqdmnumpyscipypandaspyarrowrequestsscikit-learn |
| Maintenance | Actively maintained 127 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 127,808 / month, #11,730 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software Development :: Libraries :: Application Frameworks |
Evidence: tsdb-0.8-py3-none-any.whl
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See also datasetsforecast · pypots · tsfresh · benchpots · ai4ts · pygrinder · sktime · datazets · time-aware-imputer · tsdownsample