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 = m.fit(your_df, freq='D') >>> forecast = m.predict(your_df) >>> m.plot(forecast)