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NeuralProphet 1.0.0rc8 documentation
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  • 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
    • Testing and Cross Validation
    • Plotting
    • Global Local Modelling
    • Uncertainty Quantification
    • Conditional Seasonality
    • Multiplicative Seasonality
    • Sparse Autoregression
    • Subdaily data
    • Hyperparameter Selection
    • MLflow Integration
    • Live Plotting during Training
    • Network Architecture Visualization
  • Application examples
    • Power Demand: Forecasting Load for a Hospital in SF
    • Renewable Energy: Forecasting Solar
    • Forecasting energy load with visualization
  • Migrate From Prophet
    • Migration from Prophet
    • Prophet to TorchProphet

Code Documentation

  • NeuralProphet
    • forecaster.py (NeuralProphet)
    • configure.py
    • time_dataset.py
    • time_net.py
    • torch_prophet.py
    • uncertainty.py
    • data/process.py
    • data/split.py
    • data/transform.py
    • components/router.py
    • components/base.py
    • components/future_regressors
    • components/seasonality
    • components/trend
    • plot_forecast_plotly.py
    • plot_forecast_matplotlib.py
    • plot_model_parameters_plotly.py
    • plot_model_parameters_matplotlib.py
    • utils.py
    • df_utils.py
    • hdays_utils.py
    • plot_utils.py
    • utils_metrics.py
    • utils_torch.py
    • custom_loss_metrics.py
    • logger.py
    • np_types.py

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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

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Feature guides
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Tutorial 10: Validation and Reproducibility
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