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')
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')
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.