{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/9"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"},{"label":"Image Recognition","url":"https://skillfed.io/packages/category/scientific-engineering-image-recognition"}],"enrichment":{"capability":"AutoGluon TimeSeries automates machine learning for time series forecasting, training and deploying high-accuracy models with minimal code using deep learning and statistical approaches.","skillfed_tags":["automl","time-series-forecasting","ensemble-learning"],"use_cases":["Build a demand forecasting model for supply chain without manually tuning ARIMA, Prophet, or neural network hyperparameters.","Forecast financial time series (stock prices, trading volumes) by combining statistical and deep learning models automatically.","Generate probabilistic forecasts with uncertainty intervals for risk assessment in healthcare or operations planning.","Rapidly prototype multiple forecasting approaches on new datasets to identify which model class performs best.","Deploy production forecasting pipelines that adapt to new data by retraining with minimal code changes."],"what_it_does":"AutoGluon TimeSeries is an automated machine learning library that simplifies time series forecasting by automatically selecting, training, and ensembling models from deep learning, statistical, and foundation model approaches. It wraps complex model selection and hyperparameter tuning into a few lines of code, targeting developers who want strong forecasting performance without manual model engineering.\n\nThe package depends on a large ecosystem: PyTorch and Lightning for neural networks, Hugging Face transformers and PEFT for foundation models, GluonTS and specialized forecasting libraries (statsforecast, mlforecast, chronos-forecasting) for statistical and hybrid approaches, and AutoGluon's own tabular and feature modules for preprocessing and ensemble logic. It is actively maintained, supports Python 3.10\u20133.13, and runs on Linux, macOS, and Windows.","worth_installing":"Yes, with conditions. Install if you need time series forecasting with minimal manual model selection and have the disk space and compute for PyTorch and 27 dependencies. The active maintenance, permissive license, low install friction, and zero known vulnerabilities make it a solid choice. Skip if you need lightweight, dependency-minimal forecasting or are constrained to Python <3.10."},"id":"autogluon-timeseries","links":{"html":"https://skillfed.io/packages/autogluon-timeseries","md":"https://skillfed.io/packages/autogluon-timeseries.md","pypi":"https://pypi.org/project/autogluon-timeseries/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-06","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"autogluon.timeseries","python_support":"supports_current","summary":"Fast and Accurate ML in 3 Lines of Code"},"popularity":{"monthly_downloads":347272,"position":7349,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.6.1"}