Chapter 4 - Part 1

Chapter 4 - Part 1 - Chapter 4 KVANLI PAVUR KEELING Click...

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Click to edit Master subtitle style 3/10/11 Chapter 4 Time Series Analysis and Forecasting KVANLI PAVUR KEELING
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3/10/11 Chapter Objectives At the completion of this chapter, you should be able to: ∙ Deseasonalize a time series by first calculating the seasonal indexes ∙ Discuss the nature of additive and multiplicative seasonality
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3/10/11 What is a Time Series? A time series consists of a variable (such as Sales) recorded across time Example: t Year Sales (millions of $) 1 1985 1.7 2 1986 2.4 3 1987 2.8 4 1988 3.4 . . This is y1 This is y23 This is an example of annual data
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3/10/11 Time Series Data Time series data can be: ∙ annual (one value for each year) ∙ quarterly (4 values for each year) ∙ monthly (12 values for each year) Each time series value is made up of 3 or 4 components. These are: Monthly or quarterly data only
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3/10/11 Trend Trend is the long-term growth or decline in the time series Trend usually follows a straight line Examples of linear trend are illustrated in the next two slides
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3/10/11 Linear Trends Yt t (a) Increasing trend Yt t (b) Decreasing trend
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3/10/11 Employees Example 11.0 10.0 9.0 8.0 7.0 6.0 5.0 4.0 3.0 2.0 Number of employees (thousands) | 20 00 | 20 01 | 20 02 | 20 03 | 20 04 | 20 05 | 20 06 | 20 07 t Trend We’ll take a closer look at this example in the slides to follow
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3/10/11 Curvilinear Trend Trend can also be curvilinear Curvilinear trend is also called quadratic trend Curvilinear trend is demonstrated in the following three slides In this chapter, we will pay little attention to curvilinear trend The macros will assume that trend is
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3/10/11 Examples of Curvilinear Yt t (a) Yt t (b)
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3/10/11 Examples of Curvilinear Yt t (c ) Yt t (d)
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3/10/11 An Illustration of Curvilinear Figure 4.1 300 – 200 – 100 – Power consumption (million kwh) | 200 0 | 200
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Chapter 4 - Part 1 - Chapter 4 KVANLI PAVUR KEELING Click...

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