Contents Menu Expand Light mode Dark mode Auto light/dark mode
NeuralProphet documentation
Logo
GitHub
  • Home
  • Quick Start Guide

Tutorials

  • Tutorials
    • 01: The Basics
    • 02: Trends
    • 03: Seasonality
    • 04: Auto Regression
    • 05: Lagged Regressors
    • 06: Future Regressors
    • 07: Events and Holidays
    • 08: Uncertainty
    • 09: Global Model
    • 10: Validation and Reproducibility
    • 11: Next Steps

How To Guides

  • Feature guides
    • Collect Predictions
    • Conditional Seasonality
    • Global Local Modelling
    • Live Plotting during Training
    • Network Architecture Visualization
    • Prophet to TorchProphet
    • Plotting
    • Migration from Prophet
    • Multiplicative Seasonality
    • Sparse Autoregression
    • Subdaily data
    • Testing and Cross Validation
    • Uncertainty Quantification
    • Hyperparameter Selection
  • Application examples
    • Building load forecasting: Hospital in SF
    • Renewable Energy: Forecasting hourly solar irradiance
  • Migrate From Prophet

The Science Behind

  • Model Overview
  • Presentation

API Reference

  • forecaster.py
    • configure.py
    • df_utils.py
    • hdays_utils.py
    • plot_forecast_plotly.py
    • plot_forecast_matplotlib.py
    • plot_model_parameters_plotly.py
    • plot_model_parameters_matplotlib.py
    • time_dataset.py
    • time_net.py
    • utils.py

Community

  • Contribution
  • GitHub
  • Slack
Back to top

Next steps#

  1. Browse the feature guides and application examples

  2. Read about the science behind NeuralProphet

  3. Explore the source code and API reference of NeuralProphet

  4. Join the community on Github or Slack

Next
Feature guides
Previous
Tutorial 10: Validation and Reproducibility
Copyright © 2021, Oskar Triebe
Made with Sphinx and @pradyunsg's Furo