benchpots
A Python Toolbox for Benchmarking Machine Learning on Partially-Observed Time Series
Decision gist · record as of 2026-08-14
Yes, if you are developing or comparing machine learning algorithms for partially-observed time series. The package offers low install friction, active maintenance, permissive licensing, and no known vulnerabilities. Install with caution if you have limited disk or memory—the 8 runtime dependencies (especially torch) are substantial. Not necessary for one-off time-series tasks; best suited for research and systematic benchmarking.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.8; torch and h5py may need system-level dependencies (CUDA, HDF5 libraries) depending on your environment.
- Low install friction with a pure Python wheel.
- Active maintenance with a recent commit on 2026-08-03.
License · maintenance · safety
permissive license (permissive) — BSD License (permissive) allows commercial and private use with minimal restrictions—only requires retaining copyright notice and disclaimer in distributions.
last release 2026-04-27 (109 days) · last repo commit 2026-08-03 · 44 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 122,414 downloads/mo, #11,952 on PyPI
Alternatives
Verify before relying
pip install benchpots
import benchpots
benchpots.datasets.preprocess_physionet2012(subset="all", rate=0.1)- Whether all 8 runtime dependencies (h5py, numpy, pandas, scikit-learn, torch, tsdb, pygrinder, nonlinear_benchmarks) are required for basic usage or only for specific datasets/tasks.
- Whether the package includes pre-downloaded datasets or requires separate data acquisition.
- Performance characteristics and memory footprint when working with large-scale time series.
What it is and what it does
BenchPOTS is a benchmarking toolkit for evaluating machine learning algorithms on partially-observed time series (POTS)—datasets with missing or irregularly sampled values. It provides unified preprocessing pipelines for standard POTS datasets and a suite of evaluation tasks to measure algorithm performance on problems like imputation, classification, clustering, and forecasting.
The package wraps datasets like PhysioNet2012 and applies configurable missing-data patterns (e.g., MCAR with specified rates) to create reproducible benchmarks. It depends on torch, scikit-learn, pandas, numpy, and h5py for core functionality, plus specialized time-series libraries (tsdb, pygrinder, nonlinear_benchmarks). It targets researchers and practitioners developing or comparing algorithms for incomplete time series.
Use it for
- Evaluate imputation algorithms on standard POTS datasets with controlled missing-data rates.
- Benchmark time-series forecasting models on partially-observed data from medical or sensor domains.
- Compare classification and clustering performance across multiple incomplete time-series datasets.
- Preprocess and standardize POTS datasets for reproducible machine learning research.
- Validate new time-series algorithms against established benchmarks before publication.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are developing or comparing machine learning algorithms for partially-observed time series.
The package offers low install friction, active maintenance, permissive licensing, and no known vulnerabilities. Install with caution if you have limited disk or memory—the 8 runtime dependencies (especially torch) are substantial. Not necessary for one-off time-series tasks; best suited for research and systematic benchmarking.
Install
benchpots on PyPI
Before you install
Low install friction with a pure Python wheel. Active maintenance with a recent commit on 2026-08-03. Depends on established scientific libraries (numpy, pandas, scikit-learn, torch, h5py) plus specialized time-series packages (tsdb, pygrinder, nonlinear_benchmarks).
Requires Python >=3.8; torch and h5py may need system-level dependencies (CUDA, HDF5 libraries) depending on your environment.
License in practice
BSD License (permissive) allows commercial and private use with minimal restrictions—only requires retaining copyright notice and disclaimer in distributions.
Quickstart
pip install benchpots
import benchpots
benchpots.datasets.preprocess_physionet2012(subset="all", rate=0.1)Verify before relying
- Whether all 8 runtime dependencies (h5py, numpy, pandas, scikit-learn, torch, tsdb, pygrinder, nonlinear_benchmarks) are required for basic usage or only for specific datasets/tasks.
- Whether the package includes pre-downloaded datasets or requires separate data acquisition.
- Performance characteristics and memory footprint when working with large-scale time series.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagesh5pynumpypandasscikit-learntorchtsdbpygrindernonlinear_benchmarks |
| Maintenance | Actively maintained 109 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 122,414 / month, #11,952 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: benchpots-1-py3-none-any.whl
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See also pygrinder · pypots · ai4ts · tsdb · fev · pyts · sktime · time-aware-imputer · ogb · coreforecast