時間序列 forecast()相關問題

code.R

library(tseries)
library(quantmod)
library(forecast)
library(car)
library(haven)

index3 <- getSymbols("^TWII", auto.assign = FALSE)
index.open <- na.omit(data.frame(index3[, 1]))
index.close <- na.omit(data.frame(index3[, 4]))
index.open.test <- data.frame(index.open[1:(nrow(index.open) - 365), ])
index.close.test <- data.frame(index.close[1:(nrow(index.open) - 365), ])
index.open.train <- data.frame(index.open[1:(nrow(index.open)), ])
index.close.train <- data.frame(index.close[1:(nrow(index.open)), ])
index.open.year <- data.frame(tail(index.open, 365))
index.close.year <- data.frame(tail(index.close, 365))
colnames(index.open.year) = "OP.value"
colnames(index.open.train) = "OP.value"
colnames(index.close.year) = "close.value"
colnames(index.close.train) = "close.value"
mod1 <-
  auto.arima(
    index.open.train,
    seasonal = TRUE,
    ic = "aic",
    test = "adf",
    seasonal.test = "seas",
    allowdrift = TRUE,
    allowmean = TRUE,
    stepwise = FALSE,
    approximation = FALSE
  )
mod2 <-
  auto.arima(
    index.close.train,
    seasonal = TRUE,
    ic = "aic",
    test = "adf",
    seasonal.test = "seas",
    allowdrift = TRUE,
    allowmean = TRUE,
    stepwise = FALSE,
    approximation = FALSE
  )
predict.open <-
  forecast(
    index.open.test[[1]],
    model = mod1,
    h = 365,
    include.mean = TRUE
  )
predict.close <-
  forecast(
    index.close.test[[1]],
    model = mod2,
    h = 365,
    include.mean = TRUE
  )

index.open.year[[1]]
index.close.year[[1]]
predict.close$mean
predict.open$mean 
#上面四個向量都是365長，直接做向量運算即可不必 for loop