[R] Odp: HELPPPPPP

[Petr PIKAL]

Hi

I am not an expert in time series but I believe you can check easily 
different values for arima parameters based on ACF and PACF. To give you 
better help I would need to study some literature and I believe you could 
more easily do it yourself.

Best regards
Petr

r-help-bounces at r-project.org napsal dne 30.11.2010 00:31:11:

> 
> practically we have to pass all these stages:
> he did it today with a similar case, where there is non trend, but a
> seasonality.
> I've to modify this data for canadian lynx and i know how to do it.
> the problem is to chose the correct p q d P Q D and comment the results 
for
> the graphs and the reasons of my graphs...
> 
> rm(list=ls())
> N = length(nottem)
> max_lag = 20
> plot(nottem)
> par(ask=TRUE)
> diff_12 = diff(nottem,lag=12)
> plot(diff_12)
> N = length(diff_12)
> max_lag = 36
> acf(diff_12,max_lag)
> pacf(diff_12,max_lag)
> res = arima(diff_12, order = c(1, 0, 0), seasonal = list(order = c(1, 0, 
1),
> period = 12))
> residui = res$residuals
> acf_r = acf(residui,max_lag,type="corr")
> acf_res = acf_r$acf
> Q = N * sum(acf_res[2:max_lag]^2)
> p_val = 1 - pchisq(Q, max_lag - 2)
> print(p_val)
> readline()
> 
> # Forecasting: (1) Holt Winters
> 
> m <- HoltWinters(nottem, seasonal = "add")
> p1 <- predict(m, 6, prediction.interval = TRUE)
> plot(m)
> par(ask=TRUE)
> plot(fitted(m))
> print(p1)
> 
> # 2. SARIMA
> 
> p2 = predict(arima(nottem, order = c(1, 0, 0), seasonal = list(order = 
c(1,
> 1, 1), period = 12)), n.ahead = 12)
> p3 = p2$pred
> p3 = ts(p3,start=1940,frequency=12)
> 
> # plotting both the observed series and the forecasts
> 
> final = c(nottem,p3)
> final = ts(final,start=1920,frequency=12)
> plot(final,type="b")
> lines(p3,type="b",col="red")
> 
> 
> 
> 
> 
> 
> 
> 
> 
> 
> 
> 
> 
> 
> #p2 = predict(res, n.ahead = 6)
> #x = log(AirPassengers)
> #n = length(x)
> #p2_level1 = exp(x[n-11] + (x[n] - x[n-12]) - p2$pred[1])
> #h = 6
> #p2_level = rep(0,h)
> #x = c(x,rep(0,h))
> #for (i in 1:h)
> #{
> #    p2_level[i] = exp(x[n-12+i] + (x[n-1+i] - x[n-12+(i-1)]) - 
p2$pred[i])
> #    x[n+i] = log(p2_level[i])
> #} 
> -- 
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