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
tsibble
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
Time series decomposition
3.1
Transformations and adjustments
3.2
Time series components
3.3
Moving averages
3.4
Classical decomposition
3.5
X11 decomposition
3.6
SEATS decomposition
3.7
STL decomposition
3.8
Exercises
3.9
Further reading
4
Time series features
4.1
Some simple statistics
4.2
ACF features
4.3
STL Features
4.4
Other features
4.5
Exploring Australian tourism data
4.6
Exercises
4.7
Further reading
5
The forecaster’s toolbox
5.1
A tidy forecasting workflow
5.2
Some simple forecasting methods
5.3
Fitted values and residuals
5.4
Residual diagnostics
5.5
Distributional forecasts and prediction intervals
5.6
Forecasting using transformations
5.7
Forecasting with decomposition
5.8
Evaluating point forecast accuracy
5.9
Evaluating distributional forecast accuracy
5.10
Time series cross-validation
5.11
Exercises
5.12
Further reading
6
Judgmental forecasts
6.1
Beware of limitations
6.2
Key principles
6.3
The Delphi method
6.4
Forecasting by analogy
6.5
Scenario forecasting
6.6
New product forecasting
6.7
Judgmental adjustments
6.8
Further reading
7
Time series regression models
7.1
The linear model
7.2
Least squares estimation
7.3
Evaluating the regression model
7.4
Some useful predictors
7.5
Selecting predictors
7.6
Forecasting with regression
7.7
Matrix formulation
7.8
Nonlinear regression
7.9
Correlation, causation and forecasting
7.10
Exercises
7.11
Further reading
8
Exponential smoothing
8.1
Simple exponential smoothing
8.2
Methods with trend
8.3
Methods with seasonality
8.4
A taxonomy of exponential smoothing methods
8.5
Innovations state space models for exponential smoothing
8.6
Estimation and model selection
8.7
Forecasting with ETS models
8.8
Exercises
8.9
Further reading
9
ARIMA models
9.1
Stationarity and differencing
9.2
Backshift notation
9.3
Autoregressive models
9.4
Moving average models
9.5
Non-seasonal ARIMA models
9.6
Estimation and order selection
9.7
ARIMA modelling in R
9.8
Forecasting
9.9
Seasonal ARIMA models
9.10
ARIMA vs ETS
9.11
Exercises
9.12
Further reading
10
Dynamic regression models
10.1
Estimation
10.2
Regression with ARIMA errors in R
10.3
Forecasting
10.4
Stochastic and deterministic trends
10.5
Dynamic harmonic regression
10.6
Lagged predictors
10.7
Exercises
10.8
Further reading
11
Forecasting hierarchical and grouped time series
11.1
Hierarchical and grouped time series
11.2
Single level approaches
11.3
Forecast reconciliation
11.4
Forecasting Australian domestic tourism
11.5
Reconciled distributional forecasts
11.6
Forecasting Australian prison population
11.7
Exercises
11.8
Further reading
Appendix: Using R
Appendix: For instructors
Appendix: Reviews
About the authors
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Bibliography
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Forecasting: Principles and Practice
10.8
Further reading
A detailed discussion of dynamic regression models is provided in
Pankratz
(
1991
)
.
A generalisation of dynamic regression models, known as “transfer function models,” is discussed in
Box et al.
(
2015
)
.