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NeuralProphet documentation
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  • Quick Start Guide
  • Model Overview
  • Changes from prophet
  • Trend
  • Seasonality
  • Auto-regression
  • Lagged-regression
  • Events
  • Future-regression
  • Hyperparameter-selection
  • Contribution

Feature Tutorials

  • Autoregression
  • Running benchmarking experiments
  • Prediction Collection
  • Test and CrossValidate
  • Using Lagged Covariates
  • Modeling Holidays and Special Events
  • Multiplicative Seasonality
  • Sparse Autoregression
  • Sub-daily data
  • Fitting a changing trend

Application Tutorials

  • Building load forecasting: Hospital in SF
  • Renewable Energy: Forecasting hourly solar irradiance

Code Documentations

  • configure.py
  • df_utils.py
  • forecaster.py
  • hdays.py
  • metrics.py
  • plot_forecaster.py
  • plot_model_parameters.py
  • time_dataset.py
  • time_net.py
  • utils.py

ContributeΒΆ

We compiled a Contributing to NeuralProphet page with practical instructions and further resources to help you become part of the family.

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Autoregression
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Selecting the Hyperparameters
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