search for: arima2

...set.seed(1827) ; mean(sapply(seq_len(10000), function(i) as.numeric(simulate(my.arima1, 1)) )) [1] -0.03258454 The results ("Point Forecast" versus the output of mean()) are identical to some sampling error. Now the INCORRECT model arises from adding one seasonal AR component: > my.arima2 <- Arima(x, order=c(3,0,0), seasonal=list(order=c(1,0,2), period=7), include.mean=FALSE) > forecast(my.arima2, 1) Point Forecast Lo 80 Hi 80 Lo 95 Hi 95 251 -0.1848579 -0.322421 -0.04729492 -0.3952424 0.02552655 > set.seed(1827) ; mean(sapply(seq_len(10000), fun...

...set. I would then like to build an ARIMA model on the training set and apply this model on test set. Below is some code: [CODE] data= read.table("A.txt",sep=",") attach(data) training = data[1:120, 6] test = data[121:245, 6] ts1 = ts(training) ts2 = ts(test) arima1 = arima(ts1) arima2 = arima(ts2) [/CODE] -- View this message in context: http://r.789695.n4.nabble.com/R-time-series-analysis-tp2527513p2527513.html Sent from the R help mailing list archive at Nabble.com.

arima1 = arima(data.ts[1:200], order = c(1,1,1)) arima2 = arima(data.ts[5:205], order = c(1,1,1)) arima3 = arima(data.ts[10:210], order = c(1,1,1)) arima4 = arima(data.ts[15:215], order = c(1,1,1)) arima5 = arima(data.ts[20:220], order = c(1,1,1)) arima6 = arima(data.ts[25:225], order = c(1,1,1)) arima7 = arima(data.ts[30:230], order = c(1,1,1)) arima8...

...250, -0.244477140, -0.255906978, -0.279480229) # Fit arima(p=1,d=2,q=1) Arima <- arima(x, order = c(1,2,1)) Arima$coef # Simulate from the fitted model: set.seed(1) x.sim <- arima.sim(list(order = c(1,2,1), ar = Arima$coef[1], ma = Arima$coef[2]), n = 1000, sd = sqrt(Arima$sig)) Arima2 <- arima(x.sim, order = c(1,2,1)) Arima2$coef # We recover the ar and ma coefficients but we haven't included the drift # in the simulation so the simulated series is well wide of the mark. The # following plots demonstrate how wide: par(mfrow = c(1,2)) plot(ts(x), main = "Data")...