R：如何在同一時間序列上進行 plot 多個 ARIMA 預測

Question

我想用不同的顏色對同一個 plot 進行 plot 幾個預測，但是，比例是關閉的。 我對任何其他方法持開放態度。

可重現的例子：

require(forecast)     

# MAKING DATA
data <- c(3.86000,  19.55810,  19.51091,  20.74048,  20.71333,  29.04191,  30.28864, 25.64300,  23.33368,  23.70870 , 26.16600  ,27.61286 , 27.88409 , 28.41400 , 24.81957 , 24.60952,  27.49857,  32.08000 , 29.98000, 27.49000 , 237.26150, 266.35478, 338.30000, 377.69476, 528.65905, 780.00000  )
a.ts <- ts(data,start=c(2005,1),frequency=12)

# FORECASTS
arima011_css =stats::arima(x = a.ts, order = c(0, 1, 1), method = "CSS") # css estimate
arima011_forecast = forecast(arima011_css, h=10, level=c(99.5))

arima321_css =stats::arima(x = a.ts, order = c(3, 2, 1), method = "CSS") # css estimate
arima321_forecast = forecast(arima321_css, h=10, level=c(99.5))

# MY ATTEMPT AT PLOTS
plot(arima321_forecast)
par(new=T)
plot(arima011_forecast)

Answer 1

這是類似於@jay.sf 但使用 ggplot2 的內容。

library(ggplot2)

autoplot(a.ts) +
  autolayer(arima011_forecast, series = "ARIMA(0,1,1)", alpha = 0.5) +
  autolayer(arima321_forecast, series = "ARIMA(3,2,1)", alpha = 0.5) +
  guides(colour = guide_legend("Model"))

^{由reprex package (v0.3.0) 於 2020 年 5 月 19 日創建}

Answer 2

您可以使用日期序列手動執行seq 。

rn <- format(seq.Date(as.Date("2005-01-01"), by="months", length.out=12*3), "%Y.%m")

您需要as.matrix形式的 ARIMA。

arima321_mat <- as.matrix(as.data.frame(arima321_forecast))
arima011_mat <- as.matrix(as.data.frame(arima011_forecast))

一些具有不同alpha=的 colors 。

col.1 <- rainbow(2, ,.7)
col.2 <- rainbow(2, ,.7, alpha=.2)

對於 CI，請使用polygon 。

plot(data, type="l", xlim=c(1, length(rn)), ylim=c(0, 3500), xaxt="n", main="Forecasts")
axis(1, axTicks(1), labels=F)
mtext(rn[(seq(rn)-1) %% 5 == 0], 1, 1, at=axTicks(1))
lines((length(data)+1):length(rn), arima321_mat[,1], col=col.1[1], lwd=2)
polygon(c(27:36, 36:27), c(arima321_mat[,2], rev(arima321_mat[,3])), col=col.2[1], 
        border=NA)

lines((length(data)+1):length(rn), arima011_mat[,1], col=col.1[2], lwd=3)
polygon(c(27:36, 36:27), c(arima011_mat[,2], rev(arima011_mat[,3])), col=col.2[2],
        border=NA)

legend("topleft", legend=c("ARIMA(3,2,1)", "ARIMA(0,1,1)"), col=col.1, lwd=2, cex=.9)

編輯：為避免重復lines和polygon調用，您可以使用Map將它們聯合起來。

mats <- list(arima321_mat, arima011_mat)  ## put matrices into list

plot(.)
axis(.)
mtext(.)
Map(function(i) {
  lines((length(data)+1):length(rn), mats[[i]][,1], col=col.1[i], lwd=2)
  polygon(c(27:36, 36:27), c(mats[[i]][,2], rev(mats[[i]][,3])), col=col.2[i], border=NA)
}, 1:2)
legend(.)

Answer 3

require(forecast)     

data <- c(3.86000,  19.55810,  19.51091,  20.74048,  20.71333,  29.04191,  30.28864, 25.64300,  23.33368,  23.70870 , 26.16600  ,27.61286 , 27.88409 , 28.41400 , 24.81957 , 24.60952,  27.49857,  32.08000 , 29.98000, 27.49000 , 237.26150, 266.35478, 338.30000, 377.69476, 528.65905, 780.00000  )
a.ts <- ts(data,start=c(2005,1),frequency=12)



arima011_css =stats::arima(x = a.ts, order = c(0, 1, 1), method = "CSS") # css estimate
arima011_forecast = predict(arima011_css, n.ahead = 2)$pred


arima321_css =stats::arima(x = a.ts, order = c(3, 2, 1), method = "CSS") # css estimate
arima321_forecast = predict(arima321_css, n.ahead = 2)$pred


plot(a.ts, type = "o", xlim = c(2005, 2007.5) , ylim = c(-1, 1200) , ylab = "price" ,main = "2 month Forecast")



range = c(2007+(3/12), 2007+(4/12)) # adding the dates for the prediction
lines(y = arima011_forecast , x = range ,  type = "o", col = "red")

lines(y = arima321_forecast, x = range ,  type = "o", col = "blue")