library(forecast)
data1 = read.table("d:\\FSU-school\\statapp2\\project\\4-17\\data8.txt", header=TRUE)
data1_ts = ts(data1, start=c(2006, 4), frequency = 250)
data1_ts
plot.ts(data1_ts)
length(data1_ts)

source("D:\\FSU-school\\statapp2\\project\\4-17\\sarima1")
source("D:\\FSU-school\\statapp2\\project\\4-17\\sarimasim")


y = auto.arima(data1_ts)

y = sarima.sim(760, 250, list(ar=0.9175, ma=c(-0.8598,0.0663), order=c(1,1,2)),list(order=c(0,0,0)))
y2 = arima(data1_ts, 760, order=c(1,1,2),seasonal = c(0,1,0),250)



plot()
p = predict(y, n.ahead=10)
lines(y$pred, col="red")
lines(y$pred+qnorm(.025)*p$se, col='red', lty=3)
lines(y$pred+qnorm(.975)*p$se, col='red', lty=3)