Assist with how i can solve this in r
consider sheep, the sheep population of england and wales from 1867–1939.
produce a time plot of the time series.
assume you decide to fit the following model: yt=yt−1+ϕ1(yt−1−yt−2)+ϕ2(yt−2−yt−3) +ϕ3(yt−3−yt−4)+εt, yt=yt−1+ϕ1(yt−1−yt−2)+ϕ2(yt−2−yt−3) +ϕ3(yt−3−yt−4)+εt, where εtεt is a white noise series. what sort of arima model is this (i. e., what are pp, dd, and qq)?
by examining the acf and pacf of the differenced data, explain why this model is appropriate.
the last five values of the series are given below:
year 1935 1936 1937 1938 1939
millions of sheep 1648 1665 1627 1791 1797
the estimated parameters are ϕ1=0.42ϕ1=0.42, ϕ2=−0.20ϕ2=−0.20, and ϕ3=−0.30ϕ3=−0.30. without using the forecast function, calculate forecasts for the next three years (1940–1942).
now fit the model in r and obtain the forecasts using forecast. how are they different from yours? why?