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19- 1 Chapter Nineteen McGraw-Hill/Irwin © 2005 The McGraw-Hill Companies, Inc., All Rights Reserved.

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19- 2 Chapter Nineteen Time Series and Forecasting GOALS When you have completed this chapter, you will be able to: ONE Define the four components of a time series. TWO Compute a moving average. THREE Determine a linear trend equation. FOUR Compute the trend equation for a nonlinear trend.

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19- 3 Chapter Nineteen continued Time Series and Forecasting GOALS When you have completed this chapter, you will be able to: FIVE Use trend equations to forecast future time periods and to develop seasonally adjusted forecasts. SIX Determine and interpret a set of seasonal indexes. SEVEN Deseasonalize data using a seasonal index.

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19- 4 Components of a Time Series Time Series A Time Series is a collection of data recorded over a period of time. The data may be recorded weekly, monthly, or quarterly. There are four components to a time series: The Secular Trend The Cyclical Variation The Seasonal Variation The Irregular Variation

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19- 5 Cyclical Variation Cyclical Variation The Cyclical Variation is the rise and fall of a time series over periods longer than one year.

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19- 6 The Secular Trend Secular Trend The Secular Trend is the smooth long run direction of the time series.

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19- 7 Seasonal Variation Seasonal Variation The Seasonal Variation is the pattern of change in a time series within a year. These patterns tend to repeat themselves from year to year.

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19- 8 Components of a Time Series Residual variations Residual variations are random in nature and cannot be identified. Irregular Variation The Irregular Variation is divided into two components: Episodic Variations are unpredictable, but can usually be identified, such as a flood or hurricane.

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19- 9 Linear Trend The long term trend equation (linear) : Y’ = a + bt o Y’ is the projected value of the Y variable for a selected value of t o a is the Y-intercept, the estimated value of Y when t=0. o b is the slope of the line o t is an value of time that is selected

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19- 10 Example 1 The owner of Strong Homes would like a forecast for the next couple of years of new homes that will be constructed in the Pittsburgh area. Listed below are the sales of new homes constructed in the area for the last 5 years. YearSales ($1000) 19994.3 20005.6 20017.8 20029.2 20039.7

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19- 11 Example 1 Continued EXCEL Regression tool

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19- 12 EXCEL Chart and trend line function Example 1 continued

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19- 13 The same results can be derived using MINITAB’s Stat/time series/ trend analysis function. The forecast for the year 2003 is: Y’ = 3.00 + 1.44(7) = 13.08 Example 1 continued The time series equation is: Y’ = 3.00 + 1.44t

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19- 14 Nonlinear Trends If the trend is not linear but rather the increases tend to be a constant percent, the Y values are converted to logarithms, and a least squares equation is determined using the logs.

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19- 15 Technological advances are so rapid that often initial prices decrease at an exponential rate from month to month. Hi-Tech Company provides the following information for the 12-month period after releasing its latest product. Example 2

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19- 16 Example 2 continued

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19- 17 Example 2 continued

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19- 18 Example 2 continued

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19- 19 Example 2 continued

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19- 20 Take antilog to find estimate. Thus, the estimated sales price for the 25 th period would be: Price 25 = antilog(-.0476*25+2.9596) = 58.830 Example 2 continued

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19- 21 The Moving-Average Method To apply the moving-average method to a time series, the data should follow a fairly linear trend and have a definite rhythmic pattern of fluctuations. oving-Average The M oving-Average method is used to smooth out a time series. This is accomplished by “moving” the arithmetic mean through the time series. The moving-average is the basic method used in measuring the seasonal fluctuation.

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19- 22 Seasonal Variation Typical Seasonal Indexes The numbers that result are called the Typical Seasonal Indexes. Ratio-to-Moving- Average method. The method most commonly used to compute the typical seasonal pattern is called the Ratio-to-Moving- Average method. It eliminates the trend, cyclical, and irregular components from the original data (Y).

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19- 23 Determining a Seasonal Index Using an example of sales in a large toy company, let us look at the steps in using the moving average method. WinterSpringSummerFall 19986.74.610.012.7 19996.54.69.813.6 20006.95.010.414.1 20017.05.510.815.0 20027.15.711.114.5 20038.06.211.414.9

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19- 24 Steps Step 1 Step 1 : Determine the moving total for the time series. Step 2 Step 2 : Determine the moving average for the time series. Step 3 Step 3 : The moving averages are then centered. Step 4: Step 4: The specific seasonal for each period is then computed by dividing the Y values with the centered moving averages. Step 5 Step 5 : Organize the specific seasonals in a table. Step 6 Step 6 : Apply the correction factor.

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19- 25 Deseasonalizing Data deseasonalized seasonally adjusted The resulting series (sales) is called deseasonalized sales or seasonally adjusted sales. A set of typical indexes is very useful in adjusting a series (sales, for example) The reason for deseasonalizing a series (sales) is to remove the seasonal fluctuations so that the trend and cycle can be studied.

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19- 26 Example 3 WinterSpringSummerFall 19988.78.08.78.3 19998.58.08.68.9 20009.08.79.19.2 20019.19.59.49.8 20029.39.99.79.5 200310.410.810.09.8 Deseasonalized Toy Sales

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19- 27 Example 3 continued Deseasonalized Toy Sales

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© 1999 Prentice-Hall, Inc. Chap. 15- 1 Chapter Topics Component Factors of the Time-Series Model Smoothing of Data Series Moving Averages Exponential.

© 1999 Prentice-Hall, Inc. Chap. 15- 1 Chapter Topics Component Factors of the Time-Series Model Smoothing of Data Series Moving Averages Exponential.

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