datasetsforecast
Datasets for Time series forecasting
Decision gist · record as of 2026-08-14
Yes, if you are building or benchmarking time-series forecasting models. The package eliminates manual dataset acquisition and provides standardized splits for reproducible research. Low install friction, active maintenance, and no known vulnerabilities make it a safe choice. Pre-Alpha status is not a blocker for research use, but be aware the API may change in future releases.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10.
- First load will download dataset to specified directory; ensure sufficient disk space and network access.
- Low friction: pure Python wheel, 8 common dependencies (numpy, pandas, scikit-learn, requests, aiohttp, tqdm, xlrd, utilsforecast).
License · maintenance · safety
MIT License (permissive) — MIT License (permissive): you may use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.
last release 2026-02-24 (171 days) · last repo commit 2026-08-10 · 128 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 99,701 downloads/mo, #13,014 on PyPI
Alternatives
Verify before relying
pip install datasetsforecast
from datasetsforecast.phm2008 import PHM2008
train_df, test_df = PHM2008.load(directory='data', group='FD001')- Total size of all datasets and typical download time not specified in fact sheet.
- Whether datasets are cached after first download or re-fetched on each load.
- API stability and backward compatibility guarantees given Pre-Alpha status.
What it is and what it does
datasetsforecast is a data-loading library that provides programmatic access to standard time-series forecasting benchmarks. It wraps several well-known datasets (Favorita, M3, M4, M5, Hierarchical, Longhorizon, PHM2008) and handles remote fetching and local caching, so you can load them into pandas DataFrames with a single function call. Each dataset module exposes a `load()` method that accepts a local directory and a group identifier, returning train and test splits ready for model evaluation.
The package is designed for researchers and practitioners building and benchmarking forecasting models. It depends on numpy, pandas, scikit-learn, requests, aiohttp, tqdm, xlrd, and utilsforecast—all widely used libraries. The project is actively maintained, supports Python 3.10 through 3.14, and carries a permissive MIT license. It is marked Pre-Alpha, meaning the API may change, but it has been in use since 2022 and receives regular updates.
Use it for
- Benchmark a new forecasting model against standard datasets like M4 or M5 without manual download and preprocessing.
- Build hierarchical forecasting pipelines using the Hierarchical dataset to test reconciliation methods.
- Evaluate retail demand forecasting on Favorita data for supply-chain optimization projects.
- Compare long-horizon forecasting approaches using the Longhorizon dataset.
- Prototype prognostics and health management models with PHM2008 industrial sensor data.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building or benchmarking time-series forecasting models.
The package eliminates manual dataset acquisition and provides standardized splits for reproducible research. Low install friction, active maintenance, and no known vulnerabilities make it a safe choice. Pre-Alpha status is not a blocker for research use, but be aware the API may change in future releases.
Install
datasetsforecast on PyPI
Before you install
Low friction: pure Python wheel, 8 common dependencies (numpy, pandas, scikit-learn, requests, aiohttp, tqdm, xlrd, utilsforecast). Active maintenance with recent commits; marked Pre-Alpha but in use.
Requires Python >=3.10. First load will download dataset to specified directory; ensure sufficient disk space and network access.
License in practice
MIT License (permissive): you may use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.
Quickstart
pip install datasetsforecast
from datasetsforecast.phm2008 import PHM2008
train_df, test_df = PHM2008.load(directory='data', group='FD001')Verify before relying
- Total size of all datasets and typical download time not specified in fact sheet.
- Whether datasets are cached after first download or re-fetched on each load.
- API stability and backward compatibility guarantees given Pre-Alpha status.
Package facts
| License | MIT License permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagesaiohttpnumpyscikit-learnpandasrequeststqdmutilsforecastxlrd |
| Maintenance | Actively maintained 171 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 99,701 / month, #13,014 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 2 - Pre-AlphaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseNatural Language :: EnglishProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: datasetsforecast-1.0.1-py3-none-any.whl
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See also tsdb · hierarchicalforecast · mlforecast · utilsforecast · statsforecast · neuralforecast · chronos-forecasting · timesfm · pytorch-forecasting · skforecast