Berto Jongman: Free Book Online – Forecasting Principles and Practice

[Book] Forecasting: principles and practice

• Posted by Vincent Granville on June 19, 2013 at 1:58pm in New Books and Journals
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An online textbook by Rob J Hyndman and George Athanasopoulos.

A print ver­sion of this book will be made avail­able via Ama­zon in the sec­ond half of 2013. This online ver­sion will remain freely avail­able. If you have any com­ments or sug­ges­tions on the book, feel free to add them to the com­ments at the end of the Pref­ace.

Table of Contents with Links Below the Line

• Preface
• 1 Getting started
  • 1/​​1 What can be forecast?
  • 1/2 Forecasting, planning and goals
  • 1/3 Determining what to forecast
  • 1/4 Forecasting data and methods
  • 1/5 Some case studies
  • 1/6 The basic steps in a forecasting task
  • 1/7 The statistical forecasting perspective
  • 1/8 Exercises
  • 1/9 Further reading
• 2 The forecaster’s toolbox
  • 2/1 Graphics
  • 2/2 Numerical data summaries
  • 2/3 Some simple forecasting methods
  • 2/4 Transformations and adjustments
  • 2/5 Evaluating forecast accuracy
  • 2/6 Residual diagnostics
  • 2/7 Prediction intervals
  • 2/8 Exercises
  • 2/9 Further reading
  • 2/A The forecast package in R
• 3 Judgmental forecasts
  • 3/1 Introduction
  • 3/2 Beware of limitations
  • 3/3 Key principles
  • 3/4 The Delphi method
  • 3/5 Forecasting by analogy
  • 3/6 Scenario forecasting
  • 3/7 New product forecasting
  • 3/8 Judgmental adjustments
  • 3/9 Further reading
• 4 Simple regression
  • 4/1 The simple linear model
  • 4/2 Least squares estimation
  • 4/3 Regression and correlation
  • 4/4 Evaluating the regression model
  • 4/5 Forecasting with regression
  • 4/6 Statistical inference
  • 4/7 Non-linear functional forms
  • 4/8 Regression with time series data
  • 4/9 Summary of notation and terminology
  • 4/10 Exercises
  • 4/11 Further reading
• 5 Multiple regression
  • 5/1 Introduction to multiple linear regression
  • 5/2 Some useful predictors
  • 5/3 Selecting predictors
  • 5/4 Residual diagnostics
  • 5/​​5 Matrix formulation
  • 5/6 Non-linear regression
  • 5/7 Correlation, causation and forecasting
  • 5/8 Exercises
  • 5/9 Further reading
• 6 Time series decomposition
  • 6/1 Time series components
  • 6/​2 Moving averages
  • 6/3 Classical decomposition
  • 6/​4 X-​​12-​​ARIMA decomposition
  • 6/​​5 STL decomposition
  • 6/​6 Forecasting with decomposition
  • 6/7 Exercises
  • 6/8 Further reading
• 7 Exponential smoothing
  • 7/1 Simple exponential smoothing
  • 7/2 Holt’s linear trend method
  • 7/3 Exponential trend method
  • 7/4 Damped trend methods
  • 7/5 Holt-Winters seasonal method
  • 7/6 A taxonomy of exponential smoothing methods
  • 7/7 Innovations state space models for exponential smoothing
  • 7/8 Exercises
  • 7/9 Further reading
• 8 ARIMA models
  • 8/1 Stationarity and differencing
  • 8/2 Backshift notation
  • 8/3 Autoregressive models
  • 8/4 Moving average models
  • 8/5 Non-seasonal ARIMA models
  • 8/6 Estimation and order selection
  • 8/7 ARIMA modelling in R
  • 8/8 Forecasting
  • 8/9 Seasonal ARIMA models
  • 8/10 ARIMA vs ETS
  • 8/11 Exercises
  • 8/12 Further reading
• 9 Advanced forecasting method
  • 9/1 Dynamic regression models
  • 9/2 Vector autoregressions
  • 9/3 Neural network models
  • 9/4 Forecasting hierarchical or grouped time series
  • 9/5 Further reading
• Using R