我使用以下数据加载数据框(命名为stock):
day value
2000-12-01 00:00:00 11.809242
2000-12-01 06:00:00 10.919792
2000-12-01 12:00:00 13.265208
2000-12-01 18:00:00 13.005139
2000-12-02 00:00:00 10.592222
2000-12-02 06:00:00 8.873160
2000-12-02 12:00:00 12.292847
2000-12-02 18:00:00 12.609722
2000-12-03 00:00:00 11.378299
2000-12-03 06:00:00 10.510972
2000-12-03 12:00:00 8.297222
2000-12-03 18:00:00 8.110486
2000-12-04 00:00:00 8.066154
我尝试使用arima()模型实现预测:
requires(forecast)
fit <- Arima(stock$value,c(3,1,2))
fcast <- forecast(fit, h = 5)
之后我想从预测过程中获取情节。但是我试着让x轴有日子。到现在为止我有这个:
x = as.POSIXct( stock$day , format = "%Y-%m-%d %H:%M:%S" )
plot(fcast, xaxt="n")
a = seq(x[1], by="min", length=nrow(stock))
axis(1, at = a, labels = format(a, "%Y-%m-%d %H:%M:%S"), cex.axis=0.6)
答案 0 :(得分:2)
因为你操纵时间序列对象,所以最好使用像zoo
或xts
这样的ts包。似乎forecast
包被设计用于处理这些对象。
## read the zoo object
## I add a virtual colname aaa here
## for some reason index=0:1 don't work
library(zoo)
stock <- read.zoo(text='aaa day value
2000-12-01 00:00:00 11.809242
2000-12-01 06:00:00 10.919792
2000-12-01 12:00:00 13.265208
2000-12-01 18:00:00 13.005139
2000-12-02 00:00:00 10.592222
2000-12-02 06:00:00 8.873160
2000-12-02 12:00:00 12.292847
2000-12-02 18:00:00 12.609722
2000-12-03 00:00:00 11.378299
2000-12-03 06:00:00 10.510972
2000-12-03 12:00:00 8.297222
2000-12-03 18:00:00 8.110486
2000-12-04 00:00:00 8.066154',header=TRUE,
tz='',
index=1:2)
## arima works well with zoo objects
fit <- Arima(stock,c(3,1,2))
fcast <- forecast(fit, h = 20)
plot(fcast, xaxt="n")
要绘制日期轴,您应使用axis.POSIXct
或axis.Date
。日期对象有等效的axis
。但您应该首先创建日期索引。在这里,我汇总了forecast
函数生成的原始日期和预测日期。
a <- c(as.POSIXct(index(stock)),
as.POSIXct(index(fcast$mean),origin='1970-01-01 00:00.00 UTC'))
然后我用这样的东西绘制我的轴:
## Note the use of las to rotate the axis
## you can play with format here
axis.POSIXct(1,at=a,format="%a %H",las=2,cex=0.5)