Homework3

# Homework3 - outlier>...

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Samantha Komosinski 10/16/14 Homework 3 Part One 1. In this plot, we see that the variance is not at a constant rate as time goes on, showing the data’s seasonality. We use would have to use the function HoltWinters() to execute exponential smoothing. Because of the nature of the variance, we have to use a log transformation. 2. model=HoltWinters(dat.ts, 0.2, 0.2, 0.2) 3.

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Samantha Komosinski 10/16/14 Homework 3 > plot(model) > model\$SSE [1] 13031126 4. opt=HoltWinters(dat.ts) In this model, alpha represents the level, beta represents the trend, and gamma represents seasonality. 5. > opt\$SSE [1] 9404764 The square error of this model is smaller than that of the first model, meaning that the second model is more accurate. 6. step = forecast(opt, h=12) Part Two 1. SYNA.rtn.daily=dailyReturn(SYNA.adj,type='log') 2.
Samantha Komosinski 10/16/14 Homework 3 Most of the lines in the acf plot are close to the dotted borders besides one very long line in the beginning. I believe this shows that there is autocorrelation in the data, but there may be an

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Unformatted text preview: outlier. > Box.test(SYNA.DR,20,type="Ljung-Box") Box-Ljung test data: SYNA.DR X-squared = 9.7501, df = 20, p-value = 0.9725 The box test returned a p-value of .9725, showing that is it big and there is white noise present. 3. > ar(SYNA.DR) Call: ar(x = SYNA.DR) Order selected 0 sigma^2 estimated as 0.0008431 > model=arima(SYNA.DR,order=c(0,0,0)) > model Series: SYNA.DR ARIMA(0,0,0) with non-zero mean Coefficients: intercept 1e-03 s.e. 8e-04 sigma^2 estimated as 0.0008426: log likelihood=3087.59 AIC=-6171.17 AICc=-6171.17 BIC=-6160.61 Samantha Komosinski 10/16/14 Homework 3 Since the beginning of the data strays past the borderlines to a soaring height, we can affirm that there is white noise within the autocorrelation. The p-value graph shows that the values are random. 4. Because the means are similar over time, we experience stationarity. Samantha Komosinski 10/16/14 Homework 3...
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