utilsforecast
Forecasting utilities
Decision gist · record as of 2026-08-14
Yes. Low install friction, active maintenance, permissive license, no known vulnerabilities, and a clear utility role in forecasting workflows. Install if you need to prepare, visualize, or evaluate time-series data; skip if you only need a forecasting model itself.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python ≥3.10; depends on numpy, pandas, packaging, and narwhals.
- Low install friction with a pure-Python wheel distribution.
- Active maintenance with recent releases; last commit 2026-08-07.
License · maintenance · safety
Apache Software License 2.0 (permissive) — Apache Software License 2.0 is permissive, allowing commercial use, modification, and distribution with minimal restrictions.
last release 2026-04-27 (109 days) · last repo commit 2026-08-07 · 108 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,232,563 downloads/mo, #3,198 on PyPI
Alternatives
Verify before relying
pip install utilsforecast
from utilsforecast.data import generate_series
from utilsforecast.preprocessing import fill_gaps
series = generate_series(3, with_trend=True)
filled = fill_gaps(series, freq='D')- Whether narwhals integration enables DataFrame backend switching (polars, dask, etc.) or is used internally only.
- Performance characteristics when working with large time-series datasets or many series in parallel.
What it is and what it does
utilsforecast is a utility library for time-series forecasting workflows built on top of numpy and pandas. It provides a toolkit for common forecasting tasks: generating synthetic time-series data with configurable trends and seasonality, plotting series with matplotlib, filling gaps in irregular time-series data, and evaluating forecast accuracy using standard metrics like MAPE and MASE. The library is designed to work with DataFrames in a tidy format (unique_id, ds, y columns) and integrates narwhals for potential multi-backend DataFrame support.
The package is part of the Nixtla ecosystem and sits at the utility layer—it does not itself train or produce forecasts, but rather prepares data, visualizes it, and measures model performance. It is actively maintained, supports modern Python versions (3.9–3.13, requires ≥3.10), and carries an Apache 2.0 license.
Use it for
- Generate synthetic time-series datasets for testing forecasting models or benchmarking algorithms.
- Detect and fill missing dates in irregularly sampled time-series data before model training.
- Visualize multiple time series side-by-side to inspect trends, seasonality, and anomalies.
- Evaluate forecast accuracy across multiple series using metrics like MAPE and MASE with seasonal adjustments.
- Preprocess raw time-series data into a standardized tidy format for downstream forecasting tools.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Low install friction, active maintenance, permissive license, no known vulnerabilities, and a clear utility role in forecasting workflows. Install if you need to prepare, visualize, or evaluate time-series data; skip if you only need a forecasting model itself.
Install
utilsforecast on PyPI
Before you install
Low install friction with a pure-Python wheel distribution. Active maintenance with recent releases; last commit 2026-08-07. Supports current Python versions (3.9–3.13, requires ≥3.10).
Requires Python ≥3.10; depends on numpy, pandas, packaging, and narwhals.
License in practice
Apache Software License 2.0 is permissive, allowing commercial use, modification, and distribution with minimal restrictions.
Quickstart
pip install utilsforecast
from utilsforecast.data import generate_series
from utilsforecast.preprocessing import fill_gaps
series = generate_series(3, with_trend=True)
filled = fill_gaps(series, freq='D')Verify before relying
- Whether narwhals integration enables DataFrame backend switching (polars, dask, etc.) or is used internally only.
- Performance characteristics when working with large time-series datasets or many series in parallel.
Package facts
| License | Apache Software License 2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesnumpypackagingpandasnarwhals |
| Maintenance | Actively maintained 109 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,232,563 / month, #3,198 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseNatural Language :: EnglishProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9 |
Evidence: utilsforecast-0.2.16-py3-none-any.whl
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