This is a minimalistic example of trend modelling in Neuralprophet by defining changepoints.
m = NeuralProphet( n_changepoints=100, trend_smoothness=2, yearly_seasonality=False, weekly_seasonality=False, daily_seasonality=False, ) metrics = m.fit(df, freq="D")
future = m.make_future_dataframe(df, periods=365, n_historic_predictions=len(df)) forecast = m.predict(future)
The components plot looks like below with only trend and residuals as a components.
The coefficients plot should show the coefficients corresponding to the 100 changepoints.