Time series models and forecasting

Reference no: EM13754252

Time Series Models & Forecasting

win.graph(width=4.875, height=2.5, pointsize=8)
data(larain); plot(larain,ylab='Inches',xlab='Year',type='o')

1.1 ###Scatterplot of LA Rainfall versus Last Year's LA Rainfall, Exhibit 1.2
win.graph(width=3,height=3,pointsize=8)
plot(y=larain,x=zlag(larain),ylab='Inches' , xlab='Previous Year Inches')

81_Time Series Models and Forecasting.png

1.2 Time Series Plot of Color Property from a Chemical Process

win.graph(width=4.875, height=2.5,pointsize=8)

data(color)

plot(color,ylab='Color Property',xlab='Batch',type='o')

942_Time Series Models and Forecasting1.png

1.3 Simulate a completely random process of length 48 with independent, normal values. Plot the time series plot. Does it look "random"? Repeat this exercise several times with a new simulation each time.

1.4 Simulate a completely random process of length 48 with independent, chi-square distributed values, each with 2 degrees of freedom. Display the times series plot. Does it look "random" and nonnormal? Repeat this exercise several times with a new simulation each time.

1.5 Simulate a completely random process of length 48 with independent, t-distributed values each with 5 degrees of freedom. Construct the time series plot. Does it look "random" and nonnormal? Repeat this exercise several times with a new simulation each time.

Reference no: EM13754252

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