{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"NeuralProphet is a PyTorch-based framework for interpretable time series forecasting that combines neural networks with traditional algorithms, designed for iterative model building and human-in-the-loop refinement.","skillfed_tags":["time-series","forecasting","interpretability"],"use_cases":["Build sub-daily forecasting models (e.g., hourly energy demand, sensor readings) with interpretable trend and seasonality components.","Fit a single global model across multiple related time series with partially shared parameters.","Estimate forecast uncertainty by training quantile regression models for specific percentiles.","Prototype and iterate on forecasting models interactively, inspecting component contributions and adjusting hyperparameters.","Incorporate known future events (holidays, marketing campaigns) and lagged observations as model features."],"what_it_does":"NeuralProphet is a time series forecasting framework built on PyTorch that blends neural networks with classical time-series methods. It targets developers who want to build, interpret, and iteratively refine forecasting models with minimal boilerplate. The framework includes components for autoregression, trend detection with changepoint identification, seasonality via Fourier terms, lagged and future regressors, holiday events, and quantile regression for uncertainty estimation. It is designed for sub-daily, multi-year datasets and emphasizes interpretability over out-of-the-box accuracy\u2014the expectation is that you will adjust and iterate on results.\n\nThe package depends on numpy, pandas, torch, pytorch-lightning, tensorboard, torchmetrics, and visualization libraries (matplotlib, plotly, kaleido). It supports multiple time series with shared or local model parameters and includes built-in plotting for forecasts, components, and model coefficients. The project is in beta and dormant (last release June 2024), so while the codebase is stable and community-supported, you should expect limited active development and may need to handle compatibility issues with newer PyTorch versions yourself.","worth_installing":"Yes, if you need interpretable time series forecasting with iterative refinement and don't mind the dormant maintenance status. The low install friction, permissive MIT license, and active community support make it a reasonable choice for exploratory forecasting work. However, be aware that the last release was over 784 days ago and the project is in beta\u2014if you require active bug fixes or compatibility guarantees with the latest PyTorch versions, consider alternatives or plan to contribute fixes yourself."},"id":"neuralprophet","links":{"html":"https://skillfed.io/packages/neuralprophet","md":"https://skillfed.io/packages/neuralprophet.md","pypi":"https://pypi.org/project/neuralprophet/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2024-06-21","license_spdx":null,"license_treatment":"permissive","name":"neuralprophet","python_support":"capped_below_current","summary":"NeuralProphet is an easy to learn framework for interpretable time series forecasting."},"popularity":{"monthly_downloads":96354,"position":13217,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.9.0"}