NeuralProphet

Based on Neural Networks, inspired by Facebook Prophet and AR-Net, built on Pytorch.

Why NeuralProphet?

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)

Installing

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 git@github.com:ourownstory/neural_prophet.git
$ cd neural_prophet
$ pip install .

Get started with Tutorials