Forecasting class, need someone to help me complete my hw and check my work. Rstudio

I am not sure how to do this part: Calculate a 95% prediction interval for the first forecast for each series using both methods, assuming normal errors. Compare your forecasts with those produced by R. Also, please help me to answer questions verbally.

install.packages("fpp") library(fpp)

books <- books

plot(books)

a.) Apply Holts linear method to the paperback and hardback series and compute four-day forecasts in each case.

fit <- holt(books[,1], initial = "simple", h=4) summary(fit)

b.)Compare the SSE measures of Holts method for the two series to those of simple exponential smoothing in the previous question. Discuss the merits of the two forecasting methods for these data sets.

par(mfrow=c(1,1)) plot(books[,1], main="paperback sales", xlab="days", ylab="money", xlim=c(0,40)) lines(fitted(fit), col="blue", type="o") lines(fit$mean, col="blue", type="o") legend("bottom", lty=1, col=c(1,"blue","blue"), c("data", expression(alpha -- 0.2984, beta -- 0.4984)), pch=1)

sse0hopt <- sum(residuals(fit)^2); sse0hopt

fit<- holt(books[,1], initial = "optimal", h=4) summary(fit)

plot(books[,1], main="paperback sales", xlab="days", ylab="money", xlim=c(0,40)) lines(fitted(fit), col="blue", type="o") lines(fit$mean, col="blue", type="o") legend("bottom", lty=1, col=c(1,"blue","blue"), c("data", expression(alpha -- 0.07, beta -- 0)), pch=1)

c.)Compare the forecasts for the two series using both methods. Which do you think is best?

plot(fit, xlab="days", ylab= "Money") plot(fit, xlab="days", ylab="money")

d.)Calculate a 95% prediction interval for the first forecast for each series using both methods, assuming normal errors. Compare your forecasts with those produced by R.

Thank you.