datasetsforecast

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

Downloads and loads curated time-series forecasting datasets (Favorita, M3, M4, M5, Hierarchical, Longhorizon, PHM2008) into pandas DataFrames, fetching from remote storage on first use.

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

pip install datasetsforecast

uv add datasetsforecast

poetry add datasetsforecast

Installing datasetsforecast

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.

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')

Requires Python >=3.10. First load will download dataset to specified directory; ensure sufficient disk space and network access.

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

Evidence: datasetsforecast-1.0.1-py3-none-any.whl

Keywords: time-series, forecasting, datasets

Tags

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