{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"NeuralForecast provides a collection of neural network models for time series forecasting, including RNNs, LSTMs, Transformers, and specialized architectures like NBEATS and NHITS, with sklearn-like fit/predict syntax.","skillfed_tags":["time-series-forecasting","deep-learning","neural-networks"],"use_cases":["Train and deploy NBEATS or NHITS models for univariate time series forecasting with minimal code","Incorporate exogenous variables like weather or prices into neural forecasts using built-in support","Automatically tune hyperparameters across multiple models using Ray or Optuna integration","Build probabilistic forecasts with quantile losses or parametric distributions for uncertainty quantification","Apply transfer learning to forecast new series with little historical data"],"what_it_does":"NeuralForecast is a library of neural network models for time series forecasting, built on PyTorch and PyTorch Lightning. It implements over 30 architectures ranging from classic RNNs and LSTMs to modern Transformers like Informer and iTransformer, as well as specialized models like NBEATS and NHITS. The library emphasizes usability through a familiar sklearn-style interface (.fit and .predict) and integrates with utilsforecast and coreforecast for data handling and visualization.\n\nThe package supports exogenous variables, static covariates, probabilistic forecasting with quantile losses, and automatic hyperparameter tuning via Ray and Optuna. It includes built-in interpretability methods for trend, seasonality, and exogenous components. The library is actively maintained, includes official implementations of published research (NHITS from AAAI 2023, NBEATSx from International Journal of Forecasting), and supports modern Python versions (3.10\u20133.13).","worth_installing":"Yes. NeuralForecast is worth installing for time series forecasting projects that need modern neural architectures with production-ready usability. It has active maintenance, no known vulnerabilities, permissive licensing, and low install friction. The main consideration is the substantial dependency footprint (PyTorch, Lightning, Ray, Optuna) and Python 3.10+ requirement; if your environment already supports these or you need the advanced models it provides, the tradeoff is justified."},"id":"neuralforecast","links":{"html":"https://skillfed.io/packages/neuralforecast","md":"https://skillfed.io/packages/neuralforecast.md","pypi":"https://pypi.org/project/neuralforecast/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-04","license_spdx":null,"license_treatment":"permissive","name":"neuralforecast","python_support":"supports_current","summary":"Time series forecasting suite using deep learning models"},"popularity":{"monthly_downloads":317327,"position":7662,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.2.1"}