• Forecasting: Principles and Practice
  • 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 Time series graphics
    • 2.1 ts objects
    • 2.2 Time plots
    • 2.3 Time series patterns
    • 2.4 Seasonal plots
    • 2.5 Seasonal subseries plots
    • 2.6 Scatterplots
    • 2.7 Lag plots
    • 2.8 Autocorrelation
    • 2.9 White noise
    • 2.10 Exercises
    • 2.11 Further reading
  • 3 The forecaster’s toolbox
    • 3.1 Some simple forecasting methods
    • 3.2 Transformations and adjustments
    • 3.3 Residual diagnostics
    • 3.4 Evaluating forecast accuracy
    • 3.5 Prediction intervals
    • 3.6 The forecast package in R
    • 3.7 Exercises
    • 3.8 Further reading
  • 4 Judgmental forecasts
    • 4.1 Beware of limitations
    • 4.2 Key principles
    • 4.3 The Delphi method
    • 4.4 Forecasting by analogy
    • 4.5 Scenario forecasting
    • 4.6 New product forecasting
    • 4.7 Judgmental adjustments
    • 4.8 Further reading
  • 5 Time series regression models
    • 5.1 The linear model
    • 5.2 Least squares estimation
    • 5.3 Evaluating the regression model
    • 5.4 Some useful predictors
    • 5.5 Selecting predictors
    • 5.6 Forecasting with regression
    • 5.7 Matrix formulation
    • 5.8 Nonlinear regression
    • 5.9 Correlation, causation and forecasting
    • 5.10 Exercises
    • 5.11 Further reading
  • 6 Time series decomposition
    • 6.1 Time series components
    • 6.2 Moving averages
    • 6.3 Classical decomposition
    • 6.4 X11 decomposition
    • 6.5 SEATS decomposition
    • 6.6 STL decomposition
    • 6.7 Measuring strength of trend and seasonality
    • 6.8 Forecasting with decomposition
    • 6.9 Exercises
    • 6.10 Further reading
  • 7 Exponential smoothing
    • 7.1 Simple exponential smoothing
    • 7.2 Trend methods
    • 7.3 Holt-Winters’ seasonal method
    • 7.4 A taxonomy of exponential smoothing methods
    • 7.5 Innovations state space models for exponential smoothing
    • 7.6 Estimation and model selection
    • 7.7 Forecasting with ETS models
    • 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 Dynamic regression models
    • 9.1 Estimation
    • 9.2 Regression with ARIMA errors in R
    • 9.3 Forecasting
    • 9.4 Stochastic and deterministic trends
    • 9.5 Dynamic harmonic regression
    • 9.6 Lagged predictors
    • 9.7 Exercises
    • 9.8 Further reading
  • 10 Forecasting hierarchical or grouped time series
    • 10.1 Hierarchical time series
    • 10.2 Grouped time series
    • 10.3 The bottom-up approach
    • 10.4 Top-down approaches
    • 10.5 Middle-out approach
    • 10.6 Mapping matrices
    • 10.7 The optimal reconciliation approach
    • 10.8 Exercises
    • 10.9 Further reading
  • 11 Advanced forecasting methods
    • 11.1 Complex seasonality
    • 11.2 Vector autoregressions
    • 11.3 Neural network models
    • 11.4 Bootstrapping and bagging
    • 11.5 Exercises
    • 11.6 Further reading
  • 12 Some practical forecasting issues
    • 12.1 Weekly, daily and sub-daily data
    • 12.2 Time series of counts
    • 12.3 Ensuring forecasts stay within limits
    • 12.4 Forecast combinations
    • 12.5 Prediction intervals for aggregates
    • 12.6 Backcasting
    • 12.7 Very long and very short time series
    • 12.8 Forecasting on training and test sets
    • 12.9 Dealing with missing values and outliers
    • 12.10 Further reading
  • Appendix: Using R
  • Appendix: For instructors
  • Appendix: Reviews
  • Translations
  • About the authors
  • Buy a print or downloadable version
  • Report an error
  • Bibliography
  • Published by OTexts™ with bookdown

Forecasting: Principles and Practice (2nd ed)

Appendix: Reviews

Reviews of the first edition

  • Review from Stephan Kolassa in Foresight, Fall 2010.
  • Review from Steve Miller on Information Management, April 2015
  • Review from Sandro Saitta in Swiss Analytics, April 2015, p.5. Republished at Data Mining Research.
  • Amazon reviews