{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence"}],"enrichment":{"capability":"GluonTS provides deep learning models for probabilistic time series forecasting, built on PyTorch, enabling you to train and deploy models that generate probability distributions over future values rather than point estimates.","skillfed_tags":["time-series-forecasting","deep-learning","probabilistic-modeling"],"use_cases":["Train a model on historical sales data to forecast demand with prediction intervals for inventory planning","Build probabilistic forecasts for energy consumption to quantify uncertainty in grid load predictions","Generate time series predictions for financial metrics where understanding forecast confidence is critical","Benchmark multiple deep learning architectures on your own time series dataset using available estimators","Integrate pretrained models for zero-shot forecasting on new time series without retraining"],"what_it_does":"GluonTS is a Python package for building and training probabilistic time series forecasting models using deep learning. It provides high-level estimators that learn patterns in historical time series data and generate full probability distributions over future values, rather than single-point predictions. The package handles data loading, train-test splitting, model training, and forecast generation through a unified API.\n\nTypical usage involves loading time series data into a dataset object, splitting it for training and evaluation, instantiating an estimator with forecast parameters, training on historical data, and then generating probabilistic predictions with confidence intervals. The package depends on numpy, pandas, pydantic, toolz, tqdm, and typing-extensions, making it compatible with standard data science workflows.","worth_installing":"Yes. GluonTS is actively maintained, has no known vulnerabilities, installs with low friction, and is licensed permissively. It is the right choice if you need probabilistic deep learning forecasts with confidence intervals and want a mature, well-documented framework. Install it if your use case requires neural time series models; skip it if you need only classical statistical forecasting or simple point predictions."},"id":"gluonts","links":{"html":"https://skillfed.io/packages/gluonts","md":"https://skillfed.io/packages/gluonts.md","pypi":"https://pypi.org/project/gluonts/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-31","license_spdx":null,"license_treatment":"permissive","name":"gluonts","python_support":"supports_current","summary":"Probabilistic time series modeling in Python."},"popularity":{"monthly_downloads":8522129,"position":1611,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.17.0"}