{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/2"}],"enrichment":{"capability":"Provides utilities for time-series forecasting workflows, including synthetic data generation, visualization, preprocessing (gap-filling), and evaluation metrics.","skillfed_tags":["time-series","forecasting","data-preprocessing"],"use_cases":["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."],"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.\n\nThe package is part of the Nixtla ecosystem and sits at the utility layer\u2014it 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\u20133.13, requires \u22653.10), and carries an Apache 2.0 license.","worth_installing":"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."},"id":"utilsforecast","links":{"html":"https://skillfed.io/packages/utilsforecast","md":"https://skillfed.io/packages/utilsforecast.md","pypi":"https://pypi.org/project/utilsforecast/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-27","license_spdx":null,"license_treatment":"permissive","name":"utilsforecast","python_support":"supports_current","summary":"Forecasting utilities"},"popularity":{"monthly_downloads":2232563,"position":3198,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.2.16"}