MA stands for moving average and in MA modeling we do not take into account the past values of the actual series. We consider the moving average of the past few forecast errors in this process. For identifying the orders of MA, we also need to plot acf and pacf. So let us plot the acf and pacf of the volume of StockData to evaluate the order of MA. acf can be plotted by executing the following code:
> VolumeData<-ts(StockData$Volume, frequency = 5) > acf(VolumeData, lag.max = 10)
This gives the following acf plot:
Figure 4.7: acf plot of volume
Let us plot the pacf plot of volume by executing the following code:
> pacf(VolumeData, lag.max = 10)
This gives the following plot:
Figure 4.8: pacf plot of volume
After evaluating the preceding plots, the acf cuts sharply after lag1 so the order of MA is 1.