[R] SARIMA 모형을 이용한 시계열 예측

개인 과제 코드 저장용입니다. 불펌을 금지합니다.

install.packages('TTR')
install.packages('forecast')
install.packages('ggfortify')
install.packages('tseries')
install.packages('urca')

library(TTR)
library(forecast)
library(ggfortify)
library(tseries)
library(urca)


#정상적 시계열화
data<-read .csv="" 1="" acf="" adf.test="" autoplot="" data="" filename.csv="" frequency="12," ggtitle="" ggtsdisplay="" pacf="" raw="" start="c(1981," stationary="" summary="" test="" ts="" ur.kpss="" xlab="" y="" ylab="">계절차분 stationary test
adf.test(diff(log(y))) #대립가설 : 안정성
summary(ur.kpss(diff(log(y),12))) #귀무가설 : 안정성


###############################
ndiffs(diff(log(y)))    # 차분이 필요한가?
nsdiffs(diff(log(y)))   # 계절차분이 필요한가?
###########################

plot(diff(log(y),12))
plot(diff(diff(log(y),12)))

auto.arima(diff(diff(log(y),12)))
acf(diff(diff(log(y),12)))
pacf(diff(diff(log(y),12)))
autoarima<-auto .arima="" :="" arima1="" arima2="" auto.arima="" autoarima="" box.test="" c="" coeftest="" d="1,D=1)" diff="" exp="" ggtsdisplay="" library="" lmtest="" log="y" lty="c(1,3))" n.ahead="92)" order="c(1,1,0),seasonal" pre="" pred1="" pred2="" pred3="" pred="" predict="" residuals="" rima="" summary="" ts.plot="" tsdiag="" type="Ljung-Box" y2018="" y="">