---
id: utilsforecast
version: "0.2.16"
license: Apache Software License 2.0
license_treatment: permissive
maintenance: active
---

# utilsforecast — Forecasting utilities

License: permissive · Maintenance: active · Downloads: 2.2M/mo

## 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 above — verify before relying.

Provides utilities for time-series forecasting workflows, including synthetic data generation, visualization, preprocessing (gap-filling), and evaluation metrics.

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

pip install utilsforecast
uv add utilsforecast
poetry add utilsforecast

## Installing utilsforecast

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).

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')

Requires Python ≥3.10; depends on numpy, pandas, packaging, and narwhals.

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_current
- Install friction: low
- Maintenance: active
- Downloads: 2.2M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

time series forecasting utilities, synthetic time series data generation, forecasting evaluation metrics, time series preprocessing, gap filling time series, forecasting model evaluation, time series analysis tools, time-series, forecasting, data-preprocessing

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