1 1 © 2003 Thomson  /South-Western Slide Slides Prepared by JOHN S. LOUCKS St. Edward’s University

2 2 © 2003 Thomson  /South-Western Slide Chapter 16 Forecasting n Quantitative Approaches to Forecasting n The Components of a Time Series n Measures of Forecast Accuracy n Using Smoothing Methods in Forecasting n Using Trend Projection in Forecasting n Using Trend and Seasonal Components in Forecasting n Using Regression Analysis in Forecasting n Qualitative Approaches to Forecasting

3 3 © 2003 Thomson  /South-Western Slide Quantitative Approaches to Forecasting n Quantitative methods are based on an analysis of historical data concerning one or more time series. n A time series is a set of observations measured at successive points in time or over successive periods of time. n If the historical data used are restricted to past values of the series that we are trying to forecast, the procedure is called a time series method. n If the historical data used involve other time series that are believed to be related to the time series that we are trying to forecast, the procedure is called a causal method.

4 4 © 2003 Thomson  /South-Western Slide Time Series Methods n Three time series methods are: smoothing smoothing trend projection trend projection trend projection adjusted for seasonal influence trend projection adjusted for seasonal influence

5 5 © 2003 Thomson  /South-Western Slide Components of a Time Series n The trend component accounts for the gradual shifting of the time series over a long period of time. n Any regular pattern of sequences of values above and below the trend line is attributable to the cyclical component of the series. n The seasonal component of the series accounts for regular patterns of variability within certain time periods, such as over a year. n The irregular component of the series is caused by short-term, unanticipated and non-recurring factors that affect the values of the time series. One cannot attempt to predict its impact on the time series in advance.

6 6 © 2003 Thomson  /South-Western Slide Measures of Forecast Accuracy n Mean Squared Error The average of the squared forecast errors for the historical data is calculated. The forecasting method or parameter(s) which minimize this mean squared error is then selected. n Mean Absolute Deviation The mean of the absolute values of all forecast errors is calculated, and the forecasting method or parameter(s) which minimize this measure is selected. The mean absolute deviation measure is less sensitive to individual large forecast errors than the mean squared error measure.

7 7 © 2003 Thomson  /South-Western Slide Smoothing Methods n In cases in which the time series is fairly stable and has no significant trend, seasonal, or cyclical effects, one can use smoothing methods to average out the irregular components of the time series. n Four common smoothing methods are: Moving averages Moving averages Centered moving averages Centered moving averages Weighted moving averages Weighted moving averages Exponential smoothing Exponential smoothing

8 8 © 2003 Thomson  /South-Western Slide Smoothing Methods n Moving Average Method The moving average method consists of computing an average of the most recent n data values for the series and using this average for forecasting the value of the time series for the next period.

9 9 © 2003 Thomson  /South-Western Slide During the past ten weeks, sales of cases of Comfort brand headache medicine at Robert's Drugs have been as follows: Week Sales Week Sales Week Sales Week Sales If Robert's uses a 3-period moving average to forecast sales, what is the forecast for Week 11? Example: Robert’s Drugs

10 © 2003 Thomson  /South-Western Slide Example: Robert’s Drugs n Excel Spreadsheet Showing Input Data

11 © 2003 Thomson  /South-Western Slide Example: Robert’s Drugs n Steps to Moving Average Using Excel Step 1: Select the Tools pull-down menu. Step 2: Select the Data Analysis option. Step 3: When the Data Analysis Tools dialog appears, choose M oving Average. Step 4: When the Moving Average dialog box appears: Enter B4:B13 in the Input Range box. Enter 3 in the Interval box. Enter C4 in the Output Range box. Select OK.

12 © 2003 Thomson  /South-Western Slide Example: Robert’s Drugs n Spreadsheet Showing Results Using n = 3

13 © 2003 Thomson  /South-Western Slide Smoothing Methods n Centered Moving Average Method The centered moving average method consists of computing an average of n periods' data and associating it with the midpoint of the periods. For example, the average for periods 5, 6, and 7 is associated with period 6. This methodology is useful in the process of computing season indexes.

14 © 2003 Thomson  /South-Western Slide Smoothing Methods n Weighted Moving Average Method In the weighted moving average method for computing the average of the most recent n periods, the more recent observations are typically given more weight than older observations. For convenience, the weights usually sum to 1.

15 © 2003 Thomson  /South-Western Slide Smoothing Methods n Exponential Smoothing Using exponential smoothing, the forecast for the next period is equal to the forecast for the current period plus a proportion (  ) of the forecast error in the current period. Using exponential smoothing, the forecast for the next period is equal to the forecast for the current period plus a proportion (  ) of the forecast error in the current period. Using exponential smoothing, the forecast is calculated by: Using exponential smoothing, the forecast is calculated by:  [the actual value for the current period] +  [the actual value for the current period] + (1-  )[the forecasted value for the current period], (1-  )[the forecasted value for the current period], where the smoothing constant, , is a number between 0 and 1.

16 © 2003 Thomson  /South-Western Slide 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.

17 © 2003 Thomson  /South-Western Slide Trend Projection n Using the method of least squares, the formula for the trend projection is: T t = b 0 + b 1 t. where: T t = trend forecast for time period t where: T t = trend forecast for time period t b 1 = slope of the trend line b 1 = slope of the trend line b 0 = trend line projection for time 0 b 0 = trend line projection for time 0 b 1 = n  tY t -  t  Y t b 1 = n  tY t -  t  Y t n  t 2 - (  t ) 2 n  t 2 - (  t ) 2 where: Y t = observed value of the time series at time period t where: Y t = observed value of the time series at time period t = average of the observed values for Y t = average of the observed values for Y t = average time period for the n observations = average time period for the n observations

18 © 2003 Thomson  /South-Western Slide Example: Robert’s Drugs During the past ten weeks, sales of cases of Comfort brand headache medicine at Robert's Drugs have been as follows: Week Sales Week Sales Week Sales Week Sales If Robert's uses exponential smoothing to forecast sales, which value for the smoothing constant ,  =.1 or  =.8, gives better forecasts?

19 © 2003 Thomson  /South-Western Slide Example: Robert’s Drugs n Exponential Smoothing To evaluate the two smoothing constants, determine how the forecasted values would compare with the actual historical values in each case. Let: Y t = actual sales in week t F t = forecasted sales in week t F 1 = Y 1 = 110 F 1 = Y 1 = 110 For other weeks, F t +1 =.1 Y t +.9 F t

20 © 2003 Thomson  /South-Western Slide Example: Robert’s Drugs Exponential Smoothing (  =.1, 1 -  =.9) Exponential Smoothing (  =.1, 1 -  =.9) F 1 = 110 F 2 =.1 Y F 1 =.1(110) +.9(110) = 110 F 3 =.1 Y F 2 =.1(115) +.9(110) = F 4 =.1 Y F 3 =.1(125) +.9(110.5) = F 5 =.1 Y F 4 =.1(120) +.9(111.95) = F 6 =.1 Y F 5 =.1(125) +.9(112.76) = F 7 =.1 Y F 6 =.1(120) +.9(113.98) = F 8 =.1 Y F 7 =.1(130) +.9(114.58) = F 9 =.1 Y F 8 =.1(115) +.9(116.12) = F 10 =.1 Y F 9 =.1(110) +.9(116.01) =

21 © 2003 Thomson  /South-Western Slide Example: Robert’s Drugs Exponential Smoothing (  =.8, 1 -  =.2) Exponential Smoothing (  =.8, 1 -  =.2) F 1 = 110 F 2 =.8(110) +.2(110) = 110 F 3 =.8(115) +.2(110) = 114 F 4 =.8(125) +.2(114) = F 5 =.8(120) +.2(122.80) = F 6 =.8(125) +.2(120.56) = F 7 =.8(120) +.2(124.11) = F 8 =.8(130) +.2(120.82) = F 9 =.8(115) +.2(128.16) = F 10 =.8(110) +.2(117.63) =

22 © 2003 Thomson  /South-Western Slide Example: Robert’s Drugs n Mean Squared Error In order to determine which smoothing constant gives the better performance, calculate, for each, the mean squared error for the nine weeks of forecasts, weeks 2 through 10 by: [( Y 2 - F 2 ) 2 + ( Y 3 - F 3 ) 2 + ( Y 4 - F 4 ) ( Y 10 - F 10 ) 2 ]/9

23 © 2003 Thomson  /South-Western Slide Example: Robert’s Drugs  =.1  =.8  =.1  =.8 Week Y t F t ( Y t - F t ) 2 F t ( Y t - F t ) 2 Week Y t F t ( Y t - F t ) 2 F t ( Y t - F t ) Sum Sum Sum Sum MSE Sum/9 Sum/9 MSE Sum/9 Sum/

24 © 2003 Thomson  /South-Western Slide Example: Robert’s Drugs n Excel Spreadsheet Showing Input Data

25 © 2003 Thomson  /South-Western Slide Example: Robert’s Drugs n Steps to Exponential Smoothing Using Excel Step 1: Select the Tools pull-down menu. Step 2: Select the Data Analysis option. Step 3: When the Data Analysis Tools dialog appears, choose Exponential Smoothing. Step 4: When the Exponential Smoothing dialog box appears: Enter B4:B13 in the Input Range box. Enter 0.9 (for  = 0.1) in Damping Factor box. Enter C4 in the Output Range box. Select OK.

26 © 2003 Thomson  /South-Western Slide Example: Robert’s Drugs Spreadsheet Showing Results Using  = 0.1 Spreadsheet Showing Results Using  = 0.1

27 © 2003 Thomson  /South-Western Slide Example: Robert’s Drugs Repeating the Process for  = 0.8 Repeating the Process for  = 0.8 Step 4: When the Exponential Smoothing dialog box appears: Step 4: When the Exponential Smoothing dialog box appears: Enter B4:B13 in the Input Range box. Enter 0.2 (for  = 0.8) in Damping Factor box. Enter D4 in the Output Range box. Select OK.

28 © 2003 Thomson  /South-Western Slide Example: Robert’s Drugs Spreadsheet Showing Results Using  = 0.1 and  = 0.8 Spreadsheet Showing Results Using  = 0.1 and  = 0.8

29 © 2003 Thomson  /South-Western Slide Example: Auger’s Plumbing Service The number of plumbing repair jobs performed by Auger's Plumbing Service in each of the last nine months are listed below. Month Jobs Month Jobs Month Jobs Month Jobs Month Jobs Month Jobs March 353 June 374 September 399 March 353 June 374 September 399 April 387 July 396 October 412 April 387 July 396 October 412 May 342 August 409 November 408 May 342 August 409 November 408 Forecast the number of repair jobs Auger's will perform in December using the least squares method.

30 © 2003 Thomson  /South-Western Slide Example: Auger’s Plumbing Service n Trend Projection (month) t Y t tY t t 2 (month) t Y t tY t t 2 (Mar.) (Apr.) (Apr.) (May) (May) (June) (June) (July) (July) (Aug.) (Aug.) (Sep.) (Sep.) (Oct.) (Oct.) (Nov.) (Nov.) Sum

31 © 2003 Thomson  /South-Western Slide Example: Auger’s Plumbing Service n Trend Projection (continued) = 45/9 = 5 = 3480/9 = = 45/9 = 5 = 3480/9 = n  tY t -  t  Y t (9)(17844) - (45)(3480) n  tY t -  t  Y t (9)(17844) - (45)(3480) b 1 = = = 7.4 b 1 = = = 7.4 n  t 2 - (  t ) 2 (9)(285) - (45) 2 n  t 2 - (  t ) 2 (9)(285) - (45) 2 = (5) = = (5) = T 10 = (7.4)(10) = T 10 = (7.4)(10) =

32 © 2003 Thomson  /South-Western Slide Example: Auger’s Plumbing Service n Excel Spreadsheet Showing Input Data

33 © 2003 Thomson  /South-Western Slide Example: Auger’s Plumbing Service n Steps to Trend Projection Using Excel Step 1: Select an empty cell (B13) in the worksheet. Step 2: Select the Insert pull-down menu. Step 3: Choose the Function option. Step 4: When the Paste Function dialog box appears: Choose Statistical in Function Category box. Choose Forecast in the Function Name box. Select OK. more

34 © 2003 Thomson  /South-Western Slide Example: Auger’s Plumbing Service n Steps to Trend Projecting Using Excel (continued) Step 5: When the Forecast dialog box appears: Enter 10 in the x box (for month 10). Enter B4:B12 in the Known y’s box. Enter A4:A12 in the Known x’s box. Select OK.

35 © 2003 Thomson  /South-Western Slide Example: Auger’s Plumbing Service n Spreadsheet Showing Trend Projection for Month 10

36 © 2003 Thomson  /South-Western Slide Example: Auger’s Plumbing Service (B) Forecast for December (Month 10) using a three- period ( n = 3) weighted moving average with weights of.6,.3, and.1. Then, compare this Month 10 weighted moving average forecast with the Month 10 trend projection forecast.

37 © 2003 Thomson  /South-Western Slide Example: Auger’s Plumbing Service (B) n Three-Month Weighted Moving Average The forecast for December will be the weighted average of the preceding three months: September, October, and November. F 10 =.1 Y Sep. +.3 Y Oct. +.6 Y Nov. F 10 =.1 Y Sep. +.3 Y Oct. +.6 Y Nov. =.1(399) +.3(412) +.6(408) =.1(399) +.3(412) +.6(408) = = n Trend Projection F 10 = (from earlier slide) F 10 = (from earlier slide)

38 © 2003 Thomson  /South-Western Slide Example: Auger’s Plumbing Service (B) n Conclusion Due to the positive trend component in the time series, the trend projection produced a forecast that is more in tune with the trend that exists. The weighted moving average, even with heavy (.6) placed on the current period, produced a forecast that is lagging behind the changing data.

39 © 2003 Thomson  /South-Western Slide Forecasting with Trend and Seasonal Components n Steps of Multiplicative Time Series Model 1. Calculate the centered moving averages (CMAs). 2. Center the CMAs on integer-valued periods. 3. Determine the seasonal and irregular factors ( S t I t ). 4. Determine the average seasonal factors. 5. Scale the seasonal factors ( S t ). 6. Determine the deseasonalized data. 7. Determine a trend line of the deseasonalized data. 8. Determine the deseasonalized predictions. 9. Take into account the seasonality.

40 © 2003 Thomson  /South-Western Slide Example: Terry’s Tie Shop Business at Terry's Tie Shop can be viewed as falling into three distinct seasons: (1) Christmas (November-December); (2) Father's Day (late May - mid-June); and (3) all other times. Average weekly sales ($) during each of the three seasons during the past four years are shown on the next slide. Determine a forecast for the average weekly sales in year 5 for each of the three seasons.

41 © 2003 Thomson  /South-Western Slide Example: Terry’s Tie Shop n Past Sales ($) Year Season Season

42 © 2003 Thomson  /South-Western Slide Example: Terry’s Tie Shop Dollar Moving Scaled Dollar Moving Scaled Year Season Sales ( Y t ) Average S t I t S t Y t / S t Year Season Sales ( Y t ) Average S t I t S t Y t / S t

43 © 2003 Thomson  /South-Western Slide Example: Terry’s Tie Shop n 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: 1 st MA = ( )/3 = st MA = ( )/3 = nd MA = ( )/3 = nd MA = ( )/3 = etc.

44 © 2003 Thomson  /South-Western Slide Example: Terry’s Tie Shop n 2. Center the CMAs on integer-valued periods. The first 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.

45 © 2003 Thomson  /South-Western Slide Example: Terry’s Tie Shop n 3. Determine the seasonal and irregular factors ( S t I t ). Isolate the trend and cyclical components. For each period t, this is given by: S t I t = Y t /(Moving Average for period t ) S t I t = Y t /(Moving Average for period t )

46 © 2003 Thomson  /South-Western Slide Example: Terry’s Tie Shop n 4. Determine the average seasonal factors. Averaging all S t I t values corresponding to that season: Season 1: ( ) /3 = Season 1: ( ) /3 = Season 2: ( ) /4 = Season 2: ( ) /4 = Season 3: ( ) /3 =.587 Season 3: ( ) /3 =.587

47 © 2003 Thomson  /South-Western Slide Example: Terry’s Tie Shop n 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. Season 1: 1.180/1.002 = Season 1: 1.180/1.002 = Season 2: 1.238/1.002 = Season 2: 1.238/1.002 = Season 3:.587/1.002 =.586 Season 3:.587/1.002 =.586 Total = Total = 3.000

48 © 2003 Thomson  /South-Western Slide Example: Terry’s Tie Shop n 6. Determine the deseasonalized data. Divide the data point values, Y t, by S t. n 7. Determine a trend line of the deseasonalized data. Using the least squares method for t = 1, 2,..., 12, gives: Using the least squares method for t = 1, 2,..., 12, gives: T t = t T t = t

49 © 2003 Thomson  /South-Western Slide Example: Terry’s Tie Shop n 8. Determine the deseasonalized predictions. Substitute t = 13, 14, and 15 into the least squares equation: T 13 = (33.96)(13) = 2022 T 13 = (33.96)(13) = 2022 T 14 = (33.96)(14) = 2056 T 14 = (33.96)(14) = 2056 T 15 = (33.96)(15) = 2090 T 15 = (33.96)(15) = 2090

50 © 2003 Thomson  /South-Western Slide Example: Terry’s Tie Shop n 9. Take into account the seasonality. Multiply each deseasonalized prediction by its seasonal factor to give the following forecasts for year 5: Season 1: (1.178)(2022) = Season 1: (1.178)(2022) = Season 2: (1.236)(2056) = Season 2: (1.236)(2056) = Season 3: (.586)(2090) = Season 3: (.586)(2090) =

51 © 2003 Thomson  /South-Western Slide Qualitative Approaches to Forecasting n Delphi Approach A panel of experts, each of whom is physically separated from the others and is anonymous, is asked to respond to a sequential series of questionnaires. A panel of experts, each of whom is physically separated from the others and is anonymous, is asked to respond to a sequential series of questionnaires. After each questionnaire, the responses are tabulated and the information and opinions of the entire group are made known to each of the other panel members so that they may revise their previous forecast response. After each questionnaire, the responses are tabulated and the information and opinions of the entire group are made known to each of the other panel members so that they may revise their previous forecast response. The process continues until some degree of consensus is achieved. The process continues until some degree of consensus is achieved.

52 © 2003 Thomson  /South-Western Slide Qualitative Approaches to Forecasting n Scenario Writing Scenario writing consists of developing a conceptual scenario of the future based on a well defined set of assumptions. Scenario writing consists of developing a conceptual scenario of the future based on a well defined set of assumptions. After several different scenarios have been developed, the decision maker determines which is most likely to occur in the future and makes decisions accordingly. After several different scenarios have been developed, the decision maker determines which is most likely to occur in the future and makes decisions accordingly.

53 © 2003 Thomson  /South-Western Slide Qualitative Approaches to Forecasting n Subjective or Interactive Approaches These techniques are often used by committees or panels seeking to develop new ideas or solve complex problems. These techniques are often used by committees or panels seeking to develop new ideas or solve complex problems. They often involve "brainstorming sessions". They often involve "brainstorming sessions". It is important in such sessions that any ideas or opinions be permitted to be presented without regard to its relevancy and without fear of criticism. It is important in such sessions that any ideas or opinions be permitted to be presented without regard to its relevancy and without fear of criticism.

54 © 2003 Thomson  /South-Western Slide End of Chapter 16