Forecasting with Exponential Smoothing: the State Space Approach

Rob J Hyndman, Anne B Koehler, J Keith Ord, Ralph D Snyder (Springer, 2008).

Exponential smoothing methods have been around since the 1950s, and are still the most popular forecasting methods used in business and industry. However, a modelling framework incorporating stochastic models, likelihood calculation, prediction intervals and procedures for model selection, was not developed until relatively recently. Two key papers were Ord, Koehler and Snyder (JASA, 1997) and Hyndman, Koehler, Snyder and Grose (IJF, 2002) although there have been many others filling in some of the details.

As a result, the area of exponential smoothing has undergone a substantial revolution in the past ten years. The new “state space framework” for exponential smoothing is discussed in numerous journal articles but there has been no systematic explanation and development of the ideas. Furthermore, the notation used in the journal articles tends to change from paper to paper. Consequently, researchers and practitioners struggle to use the new models in applications.

In this book we try to bring together all of the important results in a coherent manner with consistent notation. We have written it for people wanting to apply the methods in their own area of interest as well as for researchers wanting to take the ideas in new directions.

The readership is assumed to have a statistical background at about honours level in the UK/Australian/NZ system and Masters level in the US system.

R packages and data

The R packages forecast and fable both implement the methods described in the book. The expsmooth package contains the data for the exercises and most of the examples in the book.

You can also download individual csv files for each data set.

Table of contents

Preface

Part I: Introduction

1. Basic concepts [R code]
2. Getting started [R code] [Exercise solutions]

Part II: Essentials

3. Linear innovations state space models [R code] [Exercise solutions]
4. Non-linear and heteroscedastic innovations state space models [Exercise solutions]
5. Estimation of innovations state space models [R code] [Exercise solutions]
6. Prediction distributions and intervals [R code] [Exercise solutions]
7. Selection of models

Part III: Further topics

8. Normalizing seasonal components [R code]
9. Models with regressor variables [R code]
10. Some properties of linear models
11. Reduced forms and relationships with ARIMA models
12. Linear innovations state space models with random seed states
13. Conventional state space models
14. Time series with multiple seasonal patterns (with Phillip Gould) [R code]
15. Non-linear models for positive data (with Muhammad Akram) [R code]
16. Models for count data [R code]
17. Vector exponential smoothing (with Ashton de Silva)

Part IV: Applications

18. Inventory control application [R code]
19. Conditional heteroscedasticity and applications in finance [R code]
20. Economic applications: the Beveridge-Nelson decomposition (with Chin Nam Low and Heather Anderson)

Reviews

• Review by Professor David Hand, International Statistical Review, 77(2), 315-316
• Review by Lars-Erik Oller and Par Stockhammar, International Journal of Forecasting, 26(1), 204-205
• Amazon customer reviews

Errata

This is the list of all errors that we know about. If you think you’ve spotted a new one, please let us know!

p15

In the heading for section 2.3.3, “A_d,A” should read “A_d,N”

p47

The discount matrix for the damped local level model is D=\phi(1-\alpha) and the model is stable provided |\phi(1-\alpha)|<1, or equivalently |\phi|<1 and 0 < \alpha < 2.

p51

Exercise 3.2. Change “stable” to “forecastable”.
Exercise 3.3. The variance should be \sigma^2(1+(t-1)\alpha^2).

p55

The third equation should have a plus sign:

\boldsymbol{x}_{t} = \boldsymbol{D}(\boldsymbol{x}_{t-1}) + g(\boldsymbol{x}_{t-1})\frac{y_t}{r(\boldsymbol{x}_{t-1})}.

The following equation also needs a similar correction.

p76, line4-

Replace “Classes 1-4” with “Classes 1-5”

p85

For the ETS(M,A_d,M) model, \boldsymbol{w}_1 = [1 ~ \phi]^\prime and the matrix \boldsymbol{G}_1 = \left[\begin{array}{ll} \alpha & \alpha\phi \\ \beta & \beta\phi\end{array}\right].

p90

last line: the last term should be just \varepsilon_{n+j} not \varepsilon q_{n+j}

p92

the sum on the first line should be multiplied by \sigma^2.

p102

For the ETS(M,A_d,M) model, \boldsymbol{w}_1 = [1 ~ \phi]^\prime and the matrix \boldsymbol{G}_1 = \left[\begin{array}{ll} \alpha & \alpha\phi \\ \beta & \beta\phi\end{array}\right].

p108: heading 7.2.1

Change “Section” to “Selection”.

p109: line 1

\hat{y}_{i,n_j}^{(i,j)} should be \hat{y}_{n_j}^{(i,j)}

p118: line 4

delete first “is”

p150

In the ETS(A,A_d,N) model, \boldsymbol{w} = [1 ~ \phi]^\prime and \boldsymbol{F} = \left[\begin{array}{ll} 1 & \phi \\ 0 & \phi \end{array}\right]. Similarly, in the ETS(A,A_d,A) model, the second element of \boldsymbol{w} should be \phi and the second element of the top row of \boldsymbol{F} should be \phi.

p154

In the ETS(A,A_d,N) model, the second column of \boldsymbol{D} should contain \phi(1-\alpha) and \phi(1-\beta). Similarly, in the ETS(A,A_d,A) model, the first two elements of the second column of \boldsymbol{D} should be \phi(1-\alpha) and \phi(1-\beta).

p155

In Table 10.1, the parameter range for \phi should be -1<\phi\le1.

p158

Last sentence: the second element of \boldsymbol{w} should be \phi and the second element of the top row of \boldsymbol{F} should be \phi.

p159

The first two elements of the second column of \boldsymbol{D} should be \phi(1-\alpha) and \phi(1-\beta).

p280: line 10

Change “finite” to “non-zero”.