Forecasting and time series : an applied approach = 预测与时间序列 / 3rd ed.

contents

part i

introduction

chapter 1

an introduction to forecasting 2

1.1 introduction

1.2 forecasting and time series

1.3 forecasting methods

1.4 errors in forecasting

1.5 choosing a forecasting technique

1.6 an overview of quantitative forecasting techniques

1.7 computer packages: minitab and sas

exercises


chapter 2

basic statistical concepts

2.1 populations

2.2 probability

2.3 random samples and sample statistics

2.4 continuous probability distributions

.2.5 the normal probability distribution

2.6 the t-distribution, the f-distribution, and the chi-square distribution

2.7 confidence intervals for a population mean

2.8 hypothesis testing for a population mean

exercises

part ii

forecasting by using regression analysis


chapter 3

simple linear regression

3.1 the simple linear regression model

3.2 the least squares point estimates

3.3 point estimates and point predictions

3.4 model assumptions, the mean square error, and the standard error

3.5 testing the significance of the independent variable

3.6 a confidence interval for a mean value of the dependent variable and a

prediction interval for an individual value of the dependent variable

3.7 simple coefficients of determination and correlation

3.8 an f-test for the simple linear regression model

3.9 using the computer

exercises


chapter 4

multiple regression

4.1 the linear regression model

4.2 the least squares point estimates

4.3 point estimates and point predictions

4.4 the regression assumptions and the standard error

4.5 multiple coefficients of determination and correlation

4.6 an f-test for the overall model

this is an optional section.

4.7 statistical inference for bj and multicollinearity

4.8 confidence intervals and prediction intervals

4.9 an introduction to model building

4.10 residual analysis

4.11 using the computer

exercises


chapter 5

topics in regression analysis

5.1 interaction

5.2 an f-test for a portion of a model

5.3 using dummy variables to model qualitative independent variables

5.4 advanced concepts of multicollinearity

5.5 advanced model comparison methods

5.6 stepwise regression, forward selection, backward elimination,

and maximum r2 improvement

5.7 outlying and influential observations

5.8 handling unequal variances

5.9 using the computer

exercises

iii

forecasting by using time series regression,

decomposition methods,

and exponential smoothing


chapter 6

time series regression

6.1 modeling trend by using polynomial functions

6.2 detecting autocorrelation

6.3 types of seasonal variation

this is an optional section

6.4 modeling seasonal variation by using dummy variables

and trigonometric functions

6.5 growth curve models

6.6 handling first-order autocorrelation

6.7 using the computer

exercises


chapter 7

decomposition methods

7.1 multiplicative decomposition

7.2 additive decomposition

7.3 shifting seasonal patterns

7.4 the census ii decomposition method and sas proc x11

7.5 using the computer

exercises


chapter 8

exponential smoothing

8.1 simple exponential smoothing

8.2 adaptive control procedures

8.3 double exponential smoothing

8.4 winters' method

8.5 exponential and damped trends

8.6 prediction intervals

8.7 concluding comments

8.8 using the computer

exercises

part iv

forecasting by using basic techniques

of the box-jenkins methodology

this is an optional section.

contents


chapter 9

nonseasonal box-jenkins models

and their tentative identification

9.1 stationary and nonstationary time series

9.2 the sample autocorrelation and partial

autocorrelation functions: the sac and spac

9.3 an introduction to nonseasonal modeling and forecasting

9.4 tentative identification of nonseasonal box-jenkins models

'9.5 using the computer

exercises


chapter 10

estimation, diagnostic checking, and forecasting

for nonseasonal box-jenkins models

10.1 estimation

10.2 diagnostic checking

10.3 forecasting

10.4 a case study

10.5 using the computer

exercises


chapter 11

an introduction to

box-jenkins seasonal modeling

11.1 transforming a seasonal time series into a stationary time series

11.2 two examples of seasonal modeling and forecasting

11.3 using the computer

exercises


part v

forecasting by using advanced techniques of

the box-jenkins methodology

this is an optional

chapter

general box-jenkins seasonal modeling

12.1 the general seasonal model and guidelines for tentative identification

12.2 improving an inadequate seasonal model

12.3 using the computer

exercises


chapter

using the box-jenkins methodology to

improve time series regression models

and to implement exponential smoothing

13.1 box-jenkins error term models in time series regression

13.2 seasonal intervention models

13.3 box-jenkins implementation of exponential smoothing

'13.4 using the computer

exercises


chapter 14

transfer functions and intervention models

14.1 a three-step procedure for building a transfer function model

14.2 intervention models

14.3 using the computer

exercises

appendix a

statistical tables

appendix b

references

index

this is an optional section