2 Using Forecasting Methods below, demonrtrate which method is best suited (by s

Question

2 Using Forecasting Methods below, demonrtrate which method is best suited (by showing lowest mean and mean squared error MSE) 1 naive 2 3 month moving average 3 exponential smoothing 4 trend method (fatalities is dependent variable and miles travelled is independent) 5 trend method (fatalities is dependent variable and year is independent 6 comment on results from 4 vs 5 6 10 11 the analysis has to be done for motorcycle and motor vehicle to deduce is any one particular method is better suioted for oe versus Figure 3: Motorcycle Annual Data Figure 2: Motor Vehicle Fatal Annual data 13 Vehicle Vehicles Fatalitie: Travelled Fatalities Travelled 2001 3,197 9,632 76 3270 9,552.00 3,714 9,576.04 4,028 10,122.33 4,576 10.,454 30 4,837 12,049.48 5,174 21,396.18 5,312 20,810.71 4,462 20,822 04 4,502 18.461.97 15 16 17 2001 42,196 2,795,610 2002 43,005 2,855,503 2002 42,884 2,890,221 42,836 2,964,788 43,510 2,989, 430 2004 2004 200642,708 3,014,371 2007 41,259 3,031,124 008 37,423 2,976,528 2009 33,883 2956,764 2010 21 2007 24 32,885 2,966,494 2009-US. Department of Transportation, National Highway Traffic Safety Administration, Fatality Analysis Reporting System, available at http:/www.nhtsa govfars, as of June 2012 2010-US Department of Transportation, National Highway Trafic Safety 2009-U.S. Department of Transportation, National Highway Traffic Safety Administration, Fatality Analysis Reporting System, available at http://www.nhtsa.gov/fars, as of June 2012. 2010-U.S. Department of 2017 11

Explanation / Answer

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So the best method is Exponential smoothening. As the MSE and MAD is lowest.

1. Naïve 2. 3 M MA 3. 3 M MA a= 0.5 4. Exponential Trend year Fatalities Moto miles Forecast AD SE year Fatalities Moto miles Forecast AD SE year Fatalities Moto miles Forecast AD SE a = 0.2 b= 0.4 2001 3197 9632.76 2001 3197 9632.76 2001 3197 9632.76 3197 Moto miles Fatalities S T Forecast (S+T) AD SE 2002 3270 9552 3197 73 5329 2002 3270 9552 2002 3270 9552 3197.000 9632.76 3197 3197.00 -25.00 3172.00 25.00 625.00 2003 3714 9576.04 3270 444 197136 2003 3714 9576.04 2003 3714 9576.04 3233.500 9552 3270 3191.60 -17.16 3174.44 95.56 9131.71 2004 4028 10122.33 3714 314 98596 2004 4028 10122.33 3393.667 634.3333 402378.8 2004 4028 10122.33 3473.750 554.25 307193.1 9576.04 3714 3282.35 26.00 3308.36 405.64 164546.41 2005 4576 10454.3 4028 548 300304 2005 4576 10454.3 3670.667 905.3333 819628.4 2005 4576 10454.3 3750.875 825.125 680831.3 10122.33 4028 4028.00 -24.00 4004.00 24.00 576.00 2006 4837 12049.48 4576 261 68121 2006 4837 12049.48 4106 731 534361 2006 4837 12049.48 4163.438 673.5625 453686.4 10454.3 4576 4118.40 21.76 4140.16 435.84 189956.51 2007 5174 21396.18 4837 337 113569 2007 5174 21396.18 4480.333 693.6667 481173.4 2007 5174 21396.18 4500.219 673.7813 453981.2 12049.48 4837 4837.00 -24.00 4813.00 24.00 576.00 2008 5312 20810.71 5174 138 19044 2008 5312 20810.71 4862.333 449.6667 202200.1 2008 5312 20810.71 4837.109 474.8906 225521.1 21396.18 5174 4885.20 4.88 4890.08 283.92 80610.57 2009 4462 20822.04 5312 850 722500 2009 4462 20822.04 5107.667 645.6667 416885.4 2009 4462 20822.04 5074.555 612.5547 375223.2 20810.71 5312 4974.46 38.63 5013.10 298.90 89342.64 2010 4502 18461.97 4462 40 1600 2010 4502 18461.97 4982.667 480.6667 231040.4 2010 4502 18461.97 4768.277 266.2773 70903.62 20822.04 4462 4462.00 -23.00 4439.00 23.00 529.00 333.8889 169577.7 648.619 441095.4 582.9202 366762.8 18461.97 4502 4451.60 -17.96 4433.64 68.36 4673.09 MAD MSE MAD MSE MAD MSE 168.42 54056.69 MAD MSE

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