Practical MidtermSolQ2S11

# Practical MidtermSolQ2S11 - 2 A plot of data VALUE 1.0 DATE...

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2. A) Fig 1 Plot of DATA There seems no seasonal pattern. Compare SSE of different models > model1 <- HoltWinters(ts(VALUE, frequency=12, start=c(1993, 4)), gamma = 0) > model1\$SSE [1] 4.081846 > model2 <- HoltWinters(ts(VALUE, frequency=12, start=c(1993, 4)), beta=0, gamma=0) >model2\$SSE [1] 4.082095 Conclusion: Model 1 is chosen because it has smaller SSE. Holt-Winters exponential smoothing with trend and additive seasonal component. Call: HoltWinters(x = ts(VALUE, frequency = 12, start = c(1993, 4)), gamma = 0) Smoothing parameters: alpha: 1 beta : 0 gamma: 0 Coefficients:

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[,1] a 3.6386423611 b 0.0055619172 s1 -0.0288923611 s2 -0.0176006944 s3 0.0001493056 s4 0.0121076389 s5 0.0421909722 s6 0.0323993056 s7 0.0371076389 s8 0.0250659722 s9 -0.0087256944 s10 -0.0319756944 s11 -0.0271840278 s12 -0.0346423611 B) > p<-predict(model1,2,prediction.interval=TRUE, level=0.90) > p fit upr lwr Apr 2011 3.615312 3.848242 3.382382 May 2011 3.632166 3.961579 3.302752 Forecast for Model1 Time Observed / Fitted
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## This note was uploaded on 02/23/2012 for the course STAT 443 taught by Professor Yuliagel during the Spring '09 term at Waterloo.

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Practical MidtermSolQ2S11 - 2 A plot of data VALUE 1.0 DATE...

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