Forecasting time series data with Prophet : build, improve, and optimize time series forecasting models using Meta's advanced forecasting Tool

Forecasting time series data with Prophet build, improve, and optimize time series forecasting models using Meta's advanced forecasting Tool

This book will help you get to grips with time series forecasting using the leading open source forecasting tool, Prophet. You'll learn how to implement Prophet's advanced features to build forecasting models and understand why and how to modify each of the default parameters to improve re...

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Detalles Bibliográficos
Otros Autores: Rafferty, Greg, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, England : Packt Publishing Ltd [2023]
Edición:Second edition
Materias:
Time-series analysis > Data processing.
Python (Computer program language)
Machine learning.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009731836406719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright
  • Contributors
  • Table of Contents
  • Preface
  • Part 1: Getting Started with Prophet
  • Chapter 1: The History and Development of Time Series Forecasting
  • Understanding time series forecasting
  • The problem with dependent data
  • Moving averages and exponential smoothing
  • ARIMA
  • ARCH/GARCH
  • Neural networks
  • Prophet
  • Recent developments
  • NeuralProphet
  • Google's "robust time series forecasting at scale"
  • LinkedIn's Silverkite/Greykite
  • Uber's Orbit
  • Summary
  • Chapter 2: Getting Started with 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
  • Chapter 3: How Prophet Works
  • Technical requirements
  • Facebook's motivation for building Prophet
  • Analyst-in-the-loop forecasting
  • The math behind Prophet
  • Linear growth
  • Logistic growth
  • Seasonality
  • Holidays
  • Summary
  • Part 2: Seasonality, Tuning, and Advanced Features
  • Chapter 4: Handling Non-Daily Data
  • Technical requirements
  • Using monthly data
  • Using sub-daily data
  • Using data with regular gaps
  • Summary
  • Chapter 5: Working with Seasonality
  • Technical requirements
  • Understanding additive versus multiplicative seasonality
  • Controlling seasonality with the Fourier order
  • Adding custom seasonalities
  • Adding conditional seasonalities
  • Regularizing seasonality
  • Global seasonality regularization
  • Local seasonality regularization
  • Summary
  • Chapter 6: Forecasting Holiday Effects
  • 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 7: Controlling 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
  • Creating a custom trend
  • Summary
  • Chapter 8: Influencing Trend Changepoints
  • Technical requirements
  • Automatic trend changepoint detection
  • Default changepoint detection
  • Regularizing changepoints
  • Specifying custom changepoint locations
  • Summary
  • Chapter 9: Including Additional Regressors
  • Technical requirements
  • Adding binary regressors
  • Adding continuous regressors
  • Interpreting the regressor coefficients
  • Summary
  • Chapter 10: Accounting for 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
  • The moving average
  • Error standard deviation
  • Modeling outliers as special events
  • Modeling shocks such as COVID-19 lockdowns
  • Summary
  • Chapter 11: Managing Uncertainty Intervals
  • Technical requirements
  • Modeling uncertainty in trends
  • Modeling uncertainty in seasonality
  • Summary
  • Part 3: Diagnostics and Evaluation
  • Chapter 12: Performing 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 13: Evaluating 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.
  • Symmetric mean absolute percent error
  • Coverage
  • Choosing the best metric
  • Creating a Prophet performance metrics DataFrame
  • Handling irregular cut-offs
  • Tuning hyperparameters with grid search
  • Summary
  • Chapter 14: 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
  • Index
  • About Packt
  • Other Books You May Enjoy.

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