1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Slides by John Loucks St. Edward’s University

2 2 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Chapter 18, Part B Forecasting n Trend Projection n Seasonality and Trend n Time Series Decomposition

3 3 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Trend Projection n If a time series exhibits a linear trend, the method of least squares may be used to determine a trend line (projection) for future forecasts. n Least squares, also used in regression analysis, determines the unique trend line forecast which minimizes the mean square error between the trend line forecasts and the actual observed values for the time series. n The independent variable is the time period and the dependent variable is the actual observed value in the time series.

4 4 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Linear Trend Regression n Using the method of least squares, the formula for the trend projection is: where: T t = linear trend forecast in period t where: T t = linear trend forecast in period t b 0 = intercept of the linear trend line b 0 = intercept of the linear trend line b 1 = slope of the linear trend line b 1 = slope of the linear trend line t = time period t = time period T t = b 0 + b 1 t

5 5 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Linear Trend Regression n For the trend projection equation T t = b 0 + b 1 t = average value of t = average values of the time series where: Y t = value of the time series in period t n = number of time periods (observations) n = number of time periods (observations)

6 6 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. The number of plumbing repair jobs performed by The number of plumbing repair jobs performed by Auger's Plumbing Service in the last nine months is listed on the right. listed on the right. Linear Trend Regression n Example: Auger’s Plumbing Service Month Jobs March 353 May 342 April 387 July 396 June 374 August 409 September 399 October 412 November 408 Month Jobs Forecast the number of repair jobs Auger's will repair jobs Auger's will perform in December perform in December using the least squares using the least squares method. method.

7 7 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Linear Trend Regression Sum Sum (Nov.) (Oct.) (Sep.) (Aug.) (July) (June) (May) (Apr.) (Mar.) (month) t Y t

8 8 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. T 10 = (7.12)(10) = Linear Trend Regression

9 9 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Forecast for December (Month 10) using a three-period ( k = 3) weighted moving average with weights of.6,.3, and.1 weights of.6,.3, and.1 for the newest to oldest for the newest to oldest data, respectively. Then, data, respectively. Then, compare this Month 10 compare this Month 10 weighted moving average weighted moving average forecast with the Month 10 forecast with the Month 10 trend projection forecast. trend projection forecast. n Example: Auger’s Plumbing Service Trend Projection Month Jobs March 353 May 342 April 387 July 396 June 374 August 409 Septem. 399 October 412 Novem. 408 Month Jobs

10 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. n Three-Month Weighted Moving Average Trend Projection F 10 = (from earlier slide) n Trend Projection F 10 =.1 Y Sep. +.3 Y Oct. +.6 Y Nov. =.1(399) +.3(412) +.6(408) =.1(399) +.3(412) +.6(408) = = The forecast for December will be the weighted The forecast for December will be the weighted average of the preceding three months: September, October, and November.

11 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Due to the positive trend component in the time series, the trend projection produced a forecast that is more in line with the trend that exists. The weighted moving average, even with heavy (.6) weight placed on the current period, produced a forecast that is lagging behind the changing data. n Conclusion Trend Projection

12 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Holt’s Linear Exponential Smoothing n Charles Holt developed a version of exponential smoothing that can be used to forecast a time series with a linear trend. n Holt’s linear exponential smoothing is often called double exponential smoothing. Forecasts for Holt’s method are obtained using two smoothing constants,  and , and three equations. Forecasts for Holt’s method are obtained using two smoothing constants,  and , and three equations.

13 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Holt’s Linear Exponential Smoothing n Equations for Holt’s Linear Exponential Smoothing where: L t = estimate of the level of time series in period t b t = estimate of the slope of time series in period t b t = estimate of the slope of time series in period t  = smoothing constant for level  = smoothing constant for slope  F t+k = forecast for k periods ahead  k = number of periods ahead to be forecast L t =  Y t + (1 –  )( L t -1 + b t -1 ) b t =  (L t – L t -1 ) + (1 –  ) b t -1 F t + k = L t + b t k

14 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. n To get the method started we need values for L 1, the estimate of the level in period 1, and b 1, the estimate of the slope in period 1. n A commonly used approach is to set L 1 = Y 1 and b 1 = Y 2 – Y 1. Holt’s Linear Exponential Smoothing

15 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Holt’s Linear Exponential Smoothing Forecast the number of plumbing jobs Auger’s will have in months April through December using will have in months April through December using Holt’s exponential Holt’s exponential smoothing method, smoothing method, with  =.1 and with  =.1 and  =.2.  =.2. n Example: Auger’s Plumbing Service Month Jobs March 353 May 342 April 387 July 396 June 374 August 409 September 399 October 412 November 408 Month Jobs

16 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Holt’s Linear Exponential Smoothing Using Smoothing Constant Values  =.1,  =.2 Using Smoothing Constant Values  =.1,  =.2 L 1 = Y 1 = 353 b 1 = Y 2 - Y 1 = = 34 b 1 = Y 2 - Y 1 = = 34 F 2 = L 1 + b 1 (1) = = 387 F 2 = L 1 + b 1 (1) = = 387 L 2 =.1( Y 2 ) +.9( L 1 + b 1 ) =.1(387) +.9( ) = 387 F 3 = L 2 + b 2 (1) = = 421 b 2 =.2( L 2 - L 1 ) +.8( b 1 ) =.2( ) +.8(34) = 34 L 3 =.1( Y 3 ) +.9( L 2 + b 2 ) =.1(342) +.9( ) = F 4 = L 3 + b 3 (1) = = b 3 =.2( L 3 – L 2 ) +.8( b 2 ) =.2( ) +.8(34) = 32.42

17 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Holt’s Linear Exponential Smoothing Using Smoothing Constant Values  =.1,  =.2 Using Smoothing Constant Values  =.1,  =.2 L 4 =.1( Y 4 ) +.9( L 3 + b 3 ) =.1(374) +.9( ) = F 5 = L 4 + b 4 (1) = = b 4 =.2( L 4 – L 3 ) +.8( b 3 ) =.2( – 413.1) +.8(32.42) = L 5 =.1( Y 5 ) +.9( L 4 + b 4 ) =.1(396) +.9( ) = F 6 = L 5 + b 5 (1) = = b 5 =.2( L 5 – L 4 ) +.8( b 4 ) =.2( – ) +.8(30.99) = L 6 =.1( Y 6 ) +.9( L 5 + b 5 ) =.1(409) +.9( ) = F 7 = L 6 + b 6 (1) = = b 6 =.2( L 6 – L 5 ) +.8( b 5 ) =.2( – ) +.8(29.52) = 27.87

18 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Using Smoothing Constant Values  =.1,  =.2 Using Smoothing Constant Values  =.1,  =.2 L 7 =.1( Y 7 ) +.9( L 6 + b 5 ) =.1(399) +.9( ) = F 8 = L 7 + b 7 (1) = = b 7 =.2( L 7 – L 6 ) +.8( b 6 ) =.2( – ) +.8(27.87) = L 8 =.1( Y 8 ) +.9( L 7 + b 6 ) =.1(412) +.9( ) = F 9 = L 8 + b 8 (1) = = b 8 =.2( L 8 – L 7 ) +.8( b 7 ) =.2( – ) +.8(25.63) = L 9 =.1( Y 9 ) +.9( L 8 + b 7 ) =.1(408) +.9( ) = F 10 = L 9 + b 9 (1) = = b 9 =.2( L 9 – L 8 ) +.8( b 8 ) =.2( – ) +.8(23.36) = Holt’s Linear Exponential Smoothing

19 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Nonlinear Trend Regression n Sometimes time series have a curvilinear or nonlinear trend. n One example is this quadratic trend equation: n A variety of nonlinear functions can be used to develop an estimate of the trend in a time series. T t = b 0 + b 1 t + b 2 t 2 n Another example is this exponential trend equation: T t = b 0 ( b 1 ) t

20 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Seasonality without Trend n To the extent that seasonality exists, we need to incorporate it into our forecasting models to ensure accurate forecasts. n We will first look at the case of a seasonal time series with no trend and then discuss how to model seasonality with trend.

21 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Seasonality without Trend Year Quarter 1 Quarter 2 Quarter 3 Quarter n Example: Umbrella Sales n Sometimes it is difficult to identify patterns in a time series presented in a table. n Plotting the time series can be very informative.

22 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Seasonality without Trend n Umbrella Sales Time Series Plot

23 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Seasonality without Trend n The time series plot does not indicate any long-term trend in sales. n However, close inspection of the plot does reveal a seasonal pattern. n The first and third quarters have moderate sales, n the second quarter the highest sales, and n the fourth quarter tends to be the lowest quarter in terms of sales.

24 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Seasonality without Trend n Recall from an earlier chapter that dummy variables can be used to deal with categorical independent variables in a multiple regression model. n We will treat the season as a categorical variable. n Recall that when a categorical variable has k levels, k – 1 dummy variables are required. n If there are four seasons, we need three dummy variables. n Qtr1 = 1 if Quarter 1, 0 otherwise n Qtr2 = 1 if Quarter 2, 0 otherwise n Qtr3 = 1 if Quarter 3, 0 otherwise

25 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Seasonality without Trend n General Form of Estimated Regression Equation is: n Estimated Regression Equation is: n The forecasts of quarterly sales in year 6 are: n Quarter 1: Sales = (1) + 57(0) + 26(0) = 124 n Quarter 2: Sales = (0) + 57(1) + 26(0) = 152 n Quarter 3: Sales = (0) + 57(0) + 26(1) = 121 n Quarter 4: Sales = (0) + 57(0) + 26(0) = 95

26 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Seasonality and Trend n We will now extend the regression approach to include situations where the time series contains both a seasonal effect and a linear trend. n We will introduce an additional independent variable to represent time.

27 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Business at Terry's Tie Shop can be viewed as falling into three distinct seasons: (1) Christmas falling into three distinct seasons: (1) Christmas (November and December); (2) Father's Day (late (November and December); (2) Father's Day (late May to mid June); and (3) all other times. May to mid June); and (3) all other times. n Example: Terry’s Tie Shop Seasonality and Trend Average weekly sales ($) during each of the three seasons during the past four years are shown on the next slide. three seasons during the past four years are shown on the next slide.

28 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Seasonality and Trend n Example: Terry’s Tie Shop Determine a forecast for the average weekly sales in year 5 for each of the three seasons. Year Season

29 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Seasonality and Trend n There are three seasons, so we will need two dummy variables. n Seas1 = 1 if Season 1, 0 otherwise n Seas2 = 1 if Season 2, 0 otherwise n General Form of Estimated Regression Equation is: n Estimated Regression Equation is:

30 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Seasonality and Trend n The forecasts of average weekly sales in the three seasons of year 5 are: Seas. 1: Sales = (1) (0) (13) Seas. 1: Sales = (1) (0) (13) = = Seas. 2: Sales = (0) (1) (14) Seas. 2: Sales = (0) (1) (14) = = Seas. 3: Sales = (0) (0) (15) Seas. 3: Sales = (0) (0) (15) = =

31 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Time Series Decomposition n Time series decomposition can be used to separate or decompose a time series into seasonal, trend, and irregular (error) components. n While this method can be used for forecasting, its primary applicability is to get a better understanding of the time series. n Understanding what is really going on with a time series often depends upon the use of deseasonalized data.

32 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Time Series Decomposition n Decomposition methods assume that the actual time series value at period t is a function of three components: trend, seasonal, and irregular. n How these three components are combined to give the observed values of the time series depends upon whether we assume the relationship is best described by an additive or a multiplicative model.

33 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Time Series Decomposition  An additive model follows the form: n Additive Model Y t = Trend t + Seasonal t + Irregular t where: Trend t = trend value at time period t Trend t = trend value at time period t Seasonal t = seasonal value at time period t Seasonal t = seasonal value at time period t Irregular t = irregular value at time period t Irregular t = irregular value at time period t  An additive model is appropriate in situations where the seasonal fluctuations do not depend upon the level of the time series.

34 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Time Series Decomposition  A multiplicative model follows the form: n Multiplicative Model Y t = Trend t x Seasonal t x Irregular t where: Trend t = trend value at time period t Trend t = trend value at time period t Seasonal t = seasonal value at time period t Seasonal t = seasonal value at time period t Irregular t = irregular value at time period t Irregular t = irregular value at time period t  A multiplicative model is appropriate, for example, if the seasonal fluctuations grow larger as the sales volume increases because of a long-term linear trend.

35 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. n Example: Terry’s Tie Shop Determine a forecast for the average weekly sales in year 5 for each of the three seasons. Year Season Time Series Decomposition

36 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. 2 nd CMA = ( )/3 = st CMA = ( )/3 = Step 1. Calculate the centered moving averages. There are three distinct seasons in each year. Hence, take a three-season moving average to eliminate seasonal and irregular factors. For example: There are three distinct seasons in each year. Hence, take a three-season moving average to eliminate seasonal and irregular factors. For example: Etc. Calculating the Seasonal Indexes

37 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Step 2. Center the CMAs on integer-valued periods. The first centered moving average computed in step 1 ( ) will be centered on season 2 of year 1. Note that the moving averages from step 1 center themselves on integer-valued periods because n is an odd number. The first centered moving average computed in step 1 ( ) will be centered on season 2 of year 1. Note that the moving averages from step 1 center themselves on integer-valued periods because n is an odd number. Calculating the Seasonal Indexes

38 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. YearSeason Dollar Sales ( Y t ) MovingAverage ( )/ Calculating the Seasonal Indexes

39 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Calculating the Seasonal Indexes n The centered moving average values tend to “smooth out” both the seasonal and irregular fluctuations in out” both the seasonal and irregular fluctuations in the time series. the time series. n The centered moving averages represent the trend in the data and any random variation that was not the data and any random variation that was not removed by using the moving averages to smooth removed by using the moving averages to smooth the data. the data.

40 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. S t I t = Y t /(Moving Average for period t ) Step 3. Determine the seasonal & irregular factors ( S t I t ). By dividing each actual by the moving average for the same time period, we identify the combined seasonal-irregular effect in the time series. By dividing each actual by the moving average for the same time period, we identify the combined seasonal-irregular effect in the time series. Calculating the Seasonal Indexes

41 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. YearSeason Dollar Sales ( Y t ) MovingAverage StItStItStItStIt / Calculating the Seasonal Indexes

42 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Season 3: ( ) /3 =.587 Season 2: ( ) /4 = Season 1: ( ) /3 = Step 4. Determine the average seasonal factors. Averaging all S t I t values corresponding to that season: Averaging all S t I t values corresponding to that season: Calculating the Seasonal Indexes

43 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Season 3:.587/1.002 =.586 Season 2: 1.238/1.002 = Season 1: 1.180/1.002 = Step 5. Scale the seasonal factors ( S t ). Average the seasonal factors = ( )/3 = Then, divide each seasonal factor by the average of the seasonal factors. Average the seasonal factors = ( )/3 = Then, divide each seasonal factor by the average of the seasonal factors Calculating the Seasonal Indexes

44 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. YearSeason Dollar Sales ( Y t ) MovingAverage StItStItStItStIt Scaled S t Calculating the Seasonal Indexes

45 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Step 6. Determine the deseasonalized data. Divide the data point values, Y t, by S t. Divide the data point values, Y t, by S t. Using the Deseasonalizing Time Series to Identify Trend

46 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. YearSeason Dollar Sales ( Y t ) MovingAverage StItStItStItStIt Scaled S t Yt/StYt/StYt/StYt/St / Using the Deseasonalizing Time Series to Identify Trend

47 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. T t = b 0 + b 1 t Step 7. Determine a trend line of the deseasonalized data. Using the least squares method for t = 1, 2,..., 12, Using the least squares method for t = 1, 2,..., 12,gives: Using the Deseasonalizing Time Series to Identify Trend Deseasonalized Sales t = t

48 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. T 13 = (33.96)(13) = 2022 T 14 = (33.96)(14) = 2056 T 15 = (33.96)(15) = 2090 Step 8. Determine the deseasonalized predictions. Substitute t = 13, 14, and 15 into the least squares equation: Using the Deseasonalizing Time Series to Identify Trend

49 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. Season 3: (.586)(2090) = 1225 Season 2: (1.236)(2056) = 2541 Season 1: (1.178)(2022) = 2382 Step 9. Take into account the seasonality. Multiply each deseasonalized prediction by its seasonal factor to give the following forecasts for year 5: Multiply each deseasonalized prediction by its seasonal factor to give the following forecasts for year 5: Seasonal Adjustments

50 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. or duplicated, or posted to a publicly accessible website, in whole or in part. End of Chapter 18, Part B