Based on Neural Networks, inspired by Facebook Prophet and AR-Net, built on Pytorch.

Why NeuralProphet?#

NeuralProphet changes the way time series modelling and forecasting is done:

  • Support for auto-regression and covariates.

  • Automatic selection of training related hyperparameters.

  • Fourier term seasonality at different periods such as yearly, daily, weekly, hourly.

  • Piecewise linear trend with optional automatic changepoint detection.

  • Plotting for forecast components, model coefficients and final predictions.

  • Support for global modeling.

  • Lagged and future regressors.

  • Sparsity of coefficients through regularization.

  • User-friendly and powerful Python package:

>>> from neuralprophet import NeuralProphet
>>> m = NeuralProphet()
>>> metrics =, freq='D')
>>> forecast = m.predict(your_df)
>>> m.plot(forecast)