我在石油和天然气行业工作。

我一直在尝试用协变量构建一个ts预测模型，模型R代码如下：

#Getting R libraries:
library(readxl)
library(ggplot2)
library(forecast)
library(timeSeries)
library(tseries)
library(MTS)

#Create a time series object:
myts <- ts(dataset, start = c(2005,1), end = c(2019,12), frequency = 12)

#Illustrate out of sample forecasting with covariates, splitting the data:
train <- window(myts, end = c(2018,12))
test <- window(myts, start = c(2019,1))

#Fitting the time series forecasting model:
covariates <- c("Income","Prices","Sites","Vehicles")
fit <- auto.arima(train[,"Volumes"], xreg = train[,covariates])

#Forecasting from test data:
mytsfcast <- forecast(fit, h = 6*12, xreg = test[,covariates])

autoplot(mytsfcast)

但是，我一直在尝试预测 12、24、36 等月份的零售量。该模型仅生成以下结果：

模型拟合结果：

请问您能否就如何让我的模型预测超出 end = c(2019,12) 提出建议。我错过了什么？