What has Changed from ProphetΒΆ

NeuralProphet has a number of added features with respect to original Prophet. They are as follows.

  • Gradient Descent for optimisation via using PyTorch as the backend.

  • Modelling autocorrelation of time series using AR-Net

  • Modelling lagged regressors using a sepearate Feed-Forward Neural Network.

  • Configurable non-linear deep layers of the FFNNs.

  • Tuneable to specific forecast horizons (greater than 1).

  • Custom losses and metrics.

Due to the modularity of the code and the extensibility supported by PyTorch, any component trainable by gradient descent can be added as a module to NeuralProphet. Using PyTorch as the backend, makes the modelling process much faster compared to original Prophet which uses Stan as the backend.