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NeuralProphet 1.0.0rc8 documentation
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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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New Tutorials#

Note

The new tutorial section is still under development and certain sections might be empty or incomplete. We are working on it and would be very glad if you provide us with feedback. See Github issue for further information.

  • 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
Next
Tutorial 1: The Basics
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Quick Start Guide
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