Forecasting time series data with Facebook Prophet build, improve, and optimize time series forecasting models using the advanced forecasting tool
Create and improve high-quality automated forecasts for time series data that have strong seasonal effects, holidays, and additional regressors using PythonKey FeaturesLearn how to use the open-source forecasting tool Facebook Prophet to improve your forecastsBuild a forecast and run diagnostics to...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham ; Mumbai :
Packt Publishing
2021.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630977606719 |
Tabla de Contenidos:
- Cover
- Title page
- Copyright and Credits
- Contributors
- Table of Contents
- Preface
- Section 1: Getting Started
- Chapter 1: The History and Development of Time Series Forecasting
- Understanding time series forecasting
- The problem with dependent data
- Moving average and exponential smoothing
- ARIMA
- ARCH/GARCH
- Neural networks
- Prophet
- Summary
- Chapter 2: Getting Started with Facebook Prophet
- Technical requirements
- Installing Prophet
- Installation on macOS
- Installation on Windows
- Installation on Linux
- Building a simple model in Prophet
- Interpreting the forecast DataFrame
- Understanding components plots
- Summary
- Section 2: Seasonality, Tuning, and Advanced Features
- Chapter 3: Non-Daily Data
- Technical requirements
- Using monthly data
- Using sub-daily data
- Using data with regular gaps
- Summary
- Chapter 4: Seasonality
- Technical requirements
- Understanding additive versus multiplicative seasonality
- Controlling seasonality with Fourier order
- Adding custom seasonalities
- Adding conditional seasonalities
- Regularizing seasonality
- Global seasonality regularization
- Local seasonality regularization
- Summary
- Chapter 5: Holidays
- Technical requirements
- Adding default country holidays
- Adding default state/province holidays
- Creating custom holidays
- Creating multi-day holidays
- Regularizing holidays
- Global holiday regularization
- Individual holiday regularization
- Summary
- Chapter 6: Growth Modes
- Technical requirements
- Applying linear growth
- Understanding the logistic function
- Saturating forecasts
- Increasing logistic growth
- Non-constant cap
- Decreasing logistic growth
- Applying flat growth
- Summary
- Chapter 7: Trend Changepoints
- Technical requirements
- Automatic trend changepoint detection.
- Default changepoint detection
- Regularizing changepoints
- Specifying custom changepoint locations
- Summary
- Chapter 8: Additional Regressors
- Technical requirements
- Adding binary regressors
- Adding continuous regressors
- Interpreting the regressor coefficients
- Summary
- Chapter 9: Outliers and Special Events
- Technical requirements
- Correcting outliers that cause seasonality swings
- Correcting outliers that cause wide uncertainty intervals
- Detecting outliers automatically
- Winsorizing
- Standard deviation
- Moving average
- Error standard deviation
- Modeling outliers as special events
- Summary
- Chapter 10: Uncertainty Intervals
- Technical requirements
- Modeling uncertainty in trends
- Modeling uncertainty in seasonality
- Summary
- Section 3: Diagnostics and Evaluation
- Chapter 11: Cross-Validation
- Technical requirements
- Performing k-fold cross-validation
- Performing forward-chaining cross-validation
- Creating the Prophet cross-validation DataFrame
- Parallelizing cross-validation
- Summary
- Chapter 12: Performance Metrics
- Technical requirements
- Understanding Prophet's metrics
- Mean squared error
- Root mean squared error
- Mean absolute error
- Mean absolute percent error
- Median absolute percent error
- Coverage
- Choosing the best metric
- Creating the Prophet performance metrics DataFrame
- Handling irregular cut-offs
- Tuning hyperparameters with grid search
- Summary
- Chapter 13: Productionalizing Prophet
- Technical requirements
- Saving a model
- Updating a fitted model
- Making interactive plots with Plotly
- Plotly forecast plot
- Plotly components plot
- Plotly single component plot
- Plotly seasonality plot
- Summary
- Why subscribe?
- Other Books You May Enjoy
- About Packt
- Index.
