Chapter 10
Introduction
to Time Series
Modeling and
Forecasting
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Section 10.1
What Is a Time
Series
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Section 10.2
Time Series
Components
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Figure 10.1 The components of a
time series
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Section 10.3
Forecasting
Using
Smoothing
Techniques
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Figure 10.2 MINITAB plot of
quarterly power loads
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Figure 10.3 MINITAB plot of quarterly
power loads and 4-point moving average
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Figure 10.4 MINITAB printout of
exponentially smoothed quarterly power
loads
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Figure 10.5 MINITAB plot of
exponentially smoothed quarterly power
loads
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Section 10.4
Forecasting:
The
Regression
Approach
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Figure 10.6 SAS scatterplot of
sales data
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Figure 10.7 SAS printout for straight-line
model of yearly sales revenue
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continued on next slide
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Figure 10.7 SAS printout for straight-line
model of yearly sales revenue (cont’d)
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Figure 10.8 SAS printout for
quarterly power load model
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continued on next slide
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Figure 10.8 SAS printout for
quarterly power load model (cont’d)
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Section 10.5
Autocorrelation
and
Autoregressive
Error Models
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Figure 10.9a Autocorrelation functions for
several first-order auto regressive error
models: Rt = f1Rt-1 + et
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Figure 10.9b Autocorrelation functions for
several first-order auto regressive error
models: Rt = f1Rt-1 + et
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Figure 10.9c Autocorrelation functions for
several first-order auto regressive error
models: Rt = f1Rt-1 + et
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Section 10.6
Other Models
for
Autocorrelated
Errors
(Optional)
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Figure 10.10 Autocorrelations for a firstorder moving average model: Rt = et + qet-1
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Figure 10.11 Autocorrelations for a
fourth-order moving average model
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Section 10.7
Constructing
Time Series
Models
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Figure 10.12 A seasonal time
series model
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Figure 10.13 Seasonal model for
quarterly data using dummy variables
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Section 10.8
Fitting Time
Series Models
with
Autoregressive
Errors
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Figure 10.14 SAS printout for
model of annual sales revenue
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Figure 10.15 MINITAB residual
plot annual sales model
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Figure 10.16 SAS printout for annual
sales model with autoregressive errors
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continued on next slide
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Figure 10.16 SAS printout for annual
sales model with autoregressive errors
(cont’d)
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Section 10.9
Forecasting
with Time
Series
Autoregressive
Models
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Figure 10.17 SAS printout of forecasts of
annual sales revenue using straight-line
model with autoregressive errors
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Section 10.10
Seasonal Time
Series
Models:
An Example
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Figure 10.18 Water usage time
series
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Figure 10.19 SAS printout for time
series model of water usage
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Figure 10.20 Forecasts of water
usage
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Section 10.11
Forecasting
Using Lagged
Values of the
Dependent
Variable
(Optional)
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