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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

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    • Collect Predictions
    • Conditional Seasonality
    • Global Local Modelling
    • Live Plotting during Training
    • Network Architecture Visualization
    • Prophet to TorchProphet
    • Plotting
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    • 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

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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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