问题如下：
Regarding the usefulness of an autoregressive (^AR) process and an autoregressive moving average process when modeling seasonal data, Which of the following statements is correct ?
选项：
A.They both include lagged terms and, therefore, better capture a relationship in motion.
B.They both specialize in capturing only the random movements in time series data.
C.The autoregressive process is the best at capturing only random movements.
D.All the above.
解释：
A is correct
考点：MA process and AR process
解析：autoregressive 模型和 autoregressive moving average 模型都可以预测周期性的数据，因为他们都使用延迟性的观测数据。
autoregressive moving average在预测random movements时更好一些。
老板你好，这个题目能不能这样理解，AR干的是通过昨天的自己预测今天的自己，MA干的是干的是昨天的自己的波动率可能会影响到今天的自己。两者都是COV-stationary的数据，至于预测random moverment，压根就算是NON- COV-stationary的时间序列，所以BC都在瞎扯。。如果是纯随机的话，应该是只有先差分在建模。。应该是section 8的内容了
