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)
NeuralProphet can be installed with pip:
$ pip install neural_prophet
If you plan to use the package in a Jupyter notebook, we recommend to install the ‘live’ version:
$ pip install neural_prophet[live]
Alternatively, you can get the most up to date version by cloning directly from GitHub:
$ git clone firstname.lastname@example.org:ourownstory/neural_prophet.git $ cd neural_prophet $ pip install .