近期做時間序列預測，無奈自帶的預測影象太渣太難看。於是用ggplot2自己調整出一張漂亮的圖。
時間序列預測圖，很能體現圖層的概念：
* 因為存在預測區間，先畫線點，會被預測區間覆蓋，所以要注意圖層順序。
* 時間序列圖，使用自帶畫圖背景輸出效果不佳，白色網格效果更好一點。
* 案例一為原始時間序列圖（圖層概念），案例二為優化預測通道後展示圖（圖例修改）

Example1

##整理出兩個畫圖資料，原始資料freq_xts（學習集和檢驗集）以及預測資料##forcastConsequence
ArimaForecastPlot <- function(freq_xts
 
, forcastConsequence){
  g <- ggplot()  
  g <- g + geom_ribbon(aes(ymin=forcastConsequence[,6], ymax=forcastConsequence[,7], x=forcastConsequence[,1]), fill="lightskyblue", alpha=0.2)  ##預測區間大
  g <- g + geom_ribbon(aes(ymin=forcastConsequence[,4], ymax=forcastConsequence[,5], x=forcastConsequence[,1]),
 
 fill="lightskyblue1", alpha=0.3)   ##預測區間小
  g <- g + geom_line(aes(x=Index, y=freq_xts), data=fortify(freq_xts), col="gray40")
  g <- g + geom_point(aes(x=Index, y=freq_xts), data=fortify(freq_xts), col="gray26",size=3,alpha=0.6)
  g <- g + geom_line(aes(x=forcastConsequence[,1], y=forcastConsequence[,3]),
 
 col="dodgerblue4",size=1)
  g <- g + geom_point(aes(x=forcastConsequence[,1], y=forcastConsequence[,3]),col="dodgerblue2",size=3,alpha=0.6)
  g <- g + theme(panel.background=element_rect(fill='transparent', color='gray'))  ##修改背景
  g <- g + labs(x="時間", y="故障數", title="時間序列預測圖")
  g
}

ArimaForecastPlot(freq_xts, forcastConsequence)

Example2

ArimaForecastGalleryPlot <- function(freq_xts, forcastGallery){
  g <- ggplot()  
  g <- g + geom_ribbon(aes(ymin=forcastGallery[,3], ymax=forcastGallery[,4], x=forcastGallery[,1]),fill="lightskyblue1", alpha=0.8)
  g <- g + geom_line(aes(x=Index, y=freq_xts), data=fortify(freq_xts), col="gray40")
  g <- g + geom_point(aes(x=Index, y=freq_xts), data=fortify(freq_xts),size=3,alpha=0.6)
  g <- g + geom_point(aes(x=forcastGallery[,1], y=forcastGallery[,2], col=forcastGallery[, 5]))
  g <- g + scale_colour_manual(values = c( "tomato", "gray40"), labels=c("錯誤", "正確"), name=" ")   ##圖例修改
  g <- g + theme(panel.background=element_rect(fill='transparent', color='gray'))  ##修改背景
  g <- g + labs(x="時間", y="故障數", title="時間序列預測通道圖")
  g
}

ArimaForecastGalleryPlot(freq_xts, forcastGallery)