# read csv
A <- read.csv(file=’Stock.csv’)
str(A)
head(A)
windows()
plot(A)
# convert to time series
B <- ts(A, frequency = 1) ###ts時間數列
str(B) ###B是Time Series
windows()
plot(B)
# extract SIC2454
price <- B[,5]
windows()
plot(price)
###在左框輸入str(price)看其結構
return <- diff(price)
windows()
plot(return)
rate <- diff(log(price))
windows()
plot(rate)
#install.packages(‘forecast’)
# automatically check ARMIA
library(forecast)
auto.arima(price)
auto.arima(return)
auto.arima(rate)
#install.packages(‘tseries’)
# unit root check
library(tseries)
adf.test(price,k=0)
adf.test(return,k=0)
adf.test(rate,k=0)
############### ACF PACF
windows()
acf(price)
windows()
pacf(price)
windows()
acf(return)
windows()
pacf(return)
windows()
acf(rate)
windows()
pacf(rate)
#### plot stock price
windows()
autoplot(price)
windows()
autoplot(return)
windows()
autoplot(rate)
########## redirect + acf+pacf
windows()
price %>% diff() %>% ggtsdisplay(main=”Return”)
windows()
rate %>% ggtsdisplay(main=”Rate of Return”) ###價格做差分就是報酬 ###價格做差分以後的數列就是定態
#### model
fit1 <- Arima(price, order=c(0,1,0))
fit1
checkresiduals(fit1)
auto.arima(fit1$residuals)
windows()
hist(fit1$residuals,breaks=30)
windows()
library(“car”)
qqPlot(fit1$residuals)
Box.test(fit1$residuals,type=’Ljung’,lag=1)
Box.test(fit1$residuals,type=’Ljung’,lag=6)
##### forecast
windows()
autoplot(forecast(fit1))
forecast(fit1)
######## another case
auto.arima(rate)
fit2 <- Arima(rate, order=c(1,1,1))
fit2
windows()
checkresiduals(fit2)
auto.arima(fit2$residuals)
windows()
acf(fit2$residuals)
windows()
pacf(fit2$residuals)
windows()
qqPlot(fit2$residuals)
Box.test(fit2$residuals,type=’Ljung’,lag=6)
windows()
autoplot(forecast(fit2),main=’XXX’)
