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Lesson 4 - Part B Forecasting

Lesson 4 - Part B Forecasting

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Lesson 4 - Part B Forecasting

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  1. Lesson 4 - Part BForecasting • Trend Projection • Regression Analysis • Trend and Seasonal Components

  2. Forecasting Methods Forecasting Methods Quantitative Qualitative Causal Time Series Smoothing Trend Projection Trend Projection Adjusted for Seasonal Influence

  3. Trend Projection • 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. • 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. • The independent variable is the time period and the dependent variable is the actual observed value in the time series.

  4. Trend Projection • Using the method of least squares, the formula for the trend projection is: Tt= b0 + b1t where: Tt = trend forecast for time period t b1 = slope of the trend line b0 = trend line projection for time 0

  5. = average time period for the n observations = average of the observed values for Yt Trend Projection • For the trend projection equation Tt= b0 + b1t where: Yt = observed value of the time series at time period t

  6. Trend Projection • Example: Auger’s Plumbing Service The number of plumbing repair jobs performed by Auger's Plumbing Service in each of the last nine months is listed on the next slide. Forecast the number of repair jobs Auger's will perform in December using the least squares method.

  7. Trend Projection • Example: Auger’s Plumbing Service MonthJobs MonthJobs August 409 March 353 April 387 September 399 May 342 October 412 June 374 November 408 July 396

  8. Although this graph shows some up and down movement over the past 9 months, the time series seems to have an overall increasing or upward trend.

  9. Trend Projection (month) tYttYtt 2 (Mar.) 1 353 353 1 (Apr.) 2 387 774 4 (May) 3 342 1026 9 (June) 4 374 1496 16 (July) 5 396 1980 25 (Aug.) 6 409 2454 36 (Sep.) 7 399 2793 49 (Oct.) 8 412 3296 64 (Nov.) 9 408 3672 81 Sum 45 3480 17844 285

  10. Trend Projection T10 = 349.667 + (7.4)(10) = 423.667

  11. Trend Projection • Example: Auger’s Plumbing Service Forecast for December (Month 10) using a three-period (n = 3) weighted moving average with weights of .6, .3, and .1 for the newest to oldest data, respec- tively. Then, compare this Month 10 weighted moving average forecast with the Month 10 trend projection forecast.

  12. Trend Projection The forecast for December will be the weighted average of the preceding three months: September, October, and November. • Three-Month Weighted Moving Average F10 = .1YSep. + .3YOct. + .6YNov. = .1(399) + .3(412) + .6(408) = 408.3 • Trend Projection F10 = 423.7 (from earlier slide)

  13. Trend Projection • 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) weight placed on the current period, produced a forecast that is lagging behind the changing data.

  14. Using MINITAB for trend projections

  15. Dependent variable Can use different types of trend models # of data points # of future forecasts needed

  16. Interpretation of slope coefficient: In every month, # of repair jobs undertaken increased by 7.4, on average.

  17. Obtaining detailed MINITAB output

  18. Trend Analysis for Jobs Fitted Trend Equation Yt = 349.667 + 7.4*t Time Jobs Trend Detrend(Jobs – Trend) 1 353 357.067 -4.0667 2 387 364.467 22.5333 3 342 371.867 -29.8667 4 374 379.267 -5.2667 5 396 386.667 9.3333 6 409 394.067 14.9333 7 399 401.467 -2.4667 8 412 408.867 3.1333 9 408 416.267 -8.2667 Forecasts Period Forecast 10 423.667 Removing trend effect from a time series

  19. Forecasting with Trend and Seasonal Components 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 (St It ). 4. Determine the average seasonal factors. • 5. Scale the seasonal factors (St ). 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.

  20. Forecasting with Trend and Seasonal Components • Example: Terry’s Tie Shop Business at Terry's Tie Shop can be viewed as falling into three distinct seasons: (1) Christmas (November and December); (2) Father's Day (late May to 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.

  21. Forecasting with Trend and Seasonal Components Season • Example: Terry’s Tie Shop Year 123 1 2 3 4 1856 2012 985 1995 2168 1072 2241 2306 1105 2280 2408 1120 Determine a forecast for the average weekly sales in year 5 for each of the three seasons.

  22. In every year sales are lowest in season 3 and sales are increased in • seasons 1 and 2. Thus, a seasonal pattern exits for these sales. • Also, it seems overall sales are increasing (on average) over these • years. Thus, a trend exits for these sales.

  23. Forecasting with Trend and Seasonal Components 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: 1st CMA = (1856 + 2012 + 985)/3 = 1617.67 2nd CMA = (2012 + 985 + 1995)/3 = 1664.00 Etc.

  24. Forecasting with Trend and Seasonal Components Step 2. Center the CMAs on integer-valued periods. The first centered moving average computed in step 1 (1617.67) 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. If n is even, we have to compute the average of consecutive moving averages to get centered moving averages.

  25. Forecasting with Trend and Seasonal Components Dollar Sales (Yt) Moving Average Year Season (1856 + 2012 + 985)/3 1856 2012 985 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 4 1617.67 1664.00 1716.00 1745.00 1827.00 1873.00 1884.00 1897.00 1931.00 1936.00 1995 2168 1072 2241 2306 1105 2280 2408 1120

  26. Using MINITAB to get centered moving averages You can specify this i.e. 4, 6, 12, … & its depend on the problem

  27. Centered Moving Average for Sales Moving Average: Length 3 Time Sales CMA Predict Error 1 1856 * * * 2 2012 1617.67 * * 3 985 1664.00 * * 4 1995 1716.00 1617.67 377.333 5 2168 1745.00 1664.00 504.000 6 1072 1827.00 1716.00 -644.000 7 2241 1873.00 1745.00 496.000 8 2306 1884.00 1827.00 479.000 9 1105 1897.00 1873.00 -768.000 10 2280 1931.00 1884.00 396.000 11 2408 1936.00 1897.00 511.000 12 1120 * 1931.00 -811.000

  28. CMA values smooth out both the seasonal and irregular fluctuations in the time series.

  29. Forecasting with Trend and Seasonal Components Step 3. Determine the seasonal & irregular factors (St It ). Isolate the trend and cyclical components. For each period t, this is given by: St It = Yt /(Moving Average for period t )

  30. Forecasting with Trend and Seasonal Components Dollar Sales (Yt) Moving Average Year Season StIt 2012/1617.67 1856 2012 985 1995 2168 1072 2241 2306 1105 2280 2408 1120 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 4 1617.67 1664.00 1716.00 1745.00 1827.00 1873.00 1884.00 1897.00 1931.00 1936.00 1.244 .592 1.163 1.242 .587 1.196 1.224 .582 1.181 1.244

  31. Forecasting with Trend and Seasonal Components Step 4. Determine the average seasonal factors. Averaging all St Itvalues corresponding to that season: Season 1: (1.163 + 1.196 + 1.181) /3 = 1.180 Season 2: (1.244 + 1.242 + 1.224 + 1.244) /4 = 1.238 Season 3: (.592 + .587 + .582) /3 = .587 3.005

  32. Forecasting with Trend and Seasonal Components Step 5. Scale the seasonal factors (St ). Average the seasonal factors = (1.180 + 1.238 + .587)/3 = 1.002. Then, divide each seasonal factor by the average of the seasonal factors. Season 1: 1.180/1.002 = 1.178 Season 2: 1.238/1.002 = 1.236 Season 3: .587/1.002 = .586 3.000 Season 2 sales (on average) 23.6% above the average annual sales. Season 1 sales (on average) 17.8% above the average annual sales. Season 3 sales (on average) 42.4% below the average annual sales.

  33. Forecasting with Trend and Seasonal Components Dollar Sales (Yt) Moving Average Scaled St Year Season StIt 1.178 1.236 .586 1.178 1.236 .586 1.178 1.236 .586 1.178 1.236 .586 1856 2012 985 1995 2168 1072 2241 2306 1105 2280 2408 1120 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 4 1617.67 1664.00 1716.00 1745.00 1827.00 1873.00 1884.00 1897.00 1931.00 1936.00 1.244 .592 1.163 1.242 .587 1.196 1.224 .582 1.181 1.244

  34. Forecasting with Trend and Seasonal Components Divide the data point values, Yt , by St . Step 6. Determine the deseasonalized data.

  35. Forecasting with Trend and Seasonal Components 1856/1.178 Dollar Sales (Yt) Moving Average Scaled St Year Season Yt/St StIt 1.178 1.236 .586 1.178 1.236 .586 1.178 1.236 .586 1.178 1.236 .586 1576 1856 2012 985 1995 2168 1072 2241 2306 1105 2280 2408 1120 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 4 1628 1681 1694 1754 1829 1902 1866 1886 1935 1948 1911 1617.67 1664.00 1716.00 1745.00 1827.00 1873.00 1884.00 1897.00 1931.00 1936.00 1.244 .592 1.163 1.242 .587 1.196 1.224 .582 1.181 1.244

  36. Although this shows some up and down movement over the past 12 seasons, the time series seems to have an upward linear trend.

  37. Forecasting with Trend and Seasonal Components Step 7. Determine a trend line of the deseasonalized data (similar to trend projections described before). Using the least squares method for t = 1, 2, ..., 12, gives: Tt = 1580.11 + 33.96t

  38. Forecasting with Trend and Seasonal Components Step 8. Determine the deseasonalized predictions. Substitute t = 13, 14, and 15 into the least squares equation: T13 = 1580.11 + (33.96)(13) = 2022 T14 = 1580.11 + (33.96)(14) = 2056 T15 = 1580.11 + (33.96)(15) = 2090

  39. Forecasting with Trend and Seasonal Components Step 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) = 2382 Season 2: (1.236)(2056) = 2541 Season 3: ( .586)(2090) = 1225

  40. If you have detailed information (i.e. in Terry’s Tie Shop example, monthly sales over the 4 year period) we can use MINITAB to carryout all these 9 steps.

  41. Depends on the problem: • # of months for a year • # of days for a week • # of hours of a working day, etc. If both trend & seasonal effects present in the time series (based on time series plot) select this. Forecasting sales for 12 months in next year # of data points in the time series