What has Changed from Prophet
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.