III priedas

# nasa vidutines zemes temperaturos kas ketvirti duomenu nuskaitymas ir atvaizdaviams grafiskai

nasa_data <- read.table("nasa_globtemp_ketv.txt", header = TRUE)

ndata <- nasa_data[1:509,6]

nasa_data[1:509,2]+(((nasa_data[1:509,3]/3)-1)*0.25)->nasa_data2

nasa_data2

plot(nasa_data2, ndata, type="l")

#vidurkis

mean(ndata)

# dispersija

sd(ndata) ^ 2

#acf analize

acf (ndata)

acf (ndata, 250)

#pacf analize

pacf (ndata)

pacf (ndata, 250)

fdGPH(ndata[1:500])

fd = fracdiff(ndata[1:500], nar=6, nma=5)

coef(fd)

fd$stderror.dpq

fd$log.likelihood

fd = fracdiff(ndata[1:500], nar=6, nma=6)

coef(fd)

fd$stderror.dpq

fd$log.likelihood

fd = fracdiff(ndata[1:500], nar=6, nma=7)

coef(fd)

fd$stderror.dpq

fd$log.likelihood

fd = fracdiff(ndata[1:500], nar=7, nma=5)

coef(fd)

fd$stderror.dpq

fd$log.likelihood

fd = fracdiff(ndata[1:500], nar=7, nma=6)

coef(fd)

fd$stderror.dpq

fd$log.likelihood

fd = fracdiff(ndata[1:500], nar=7, nma=7)

coef(fd)

fd$stderror.dpq

fd$log.likelihood

fd = fracdiff(ndata[1:500], nar=8, nma=5)

coef(fd)

fd$stderror.dpq

fd$log.likelihood

fd = fracdiff(ndata[1:500], nar=8, nma=6)

coef(fd)

fd$stderror.dpq

fd$log.likelihood

fd = fracdiff(ndata[1:500], nar=8, nma=7)

coef(fd)

fd$stderror.dpq

fd$log.likelihood

fd.arfima1 <- arima(ndata[1:500], order = c(7,0.424,7), method = "ML")

fd.arfima1

tsdiag(fd.arfima1)

fd.arfima2 <- arima(ndata[1:500], order = c(8,0.368,7), method = "ML")

fd.arfima2

tsdiag(fd.arfima2)

#realus duomenys

ndata[501:509]

fd.predict1 <- predict(fd.arfima1, n.ahead=9)

fd.predict1

sum((ndata[501:509] - fd.predict1$pred[1:9])^2)/9

sum(((abs(ndata[501:509] - fd.predict1$pred[1:9]))/ndata[501:509])*100)/9

fd.predict2 <- predict(fd.arfima2, n.ahead=9)

fd.predict2

sum((ndata[501:509] - fd.predict2$pred[1:9])^2)/9

sum(((abs(ndata[501:509] - fd.predict2$pred[1:9]))/ndata[501:509])*100)/9

plot(c(1:9), ndata[501:509], "l")

plot(c(1:9), fd.predict1$pred[1:9], "l")