R 访问时间序列中的时间元素

我正在使用R中的forecast包对十几个业务指标进行一些基本的时间序列预测

我通常根据过去几年的数据设定季度目标

在本季度的过程中，我得到了实际数据并重新预测，看看是否有重大变化，这将使我修改预期目标。我只想在平均值在统计上不同或者趋势发生了有意义的变化时修改目标，比如控制图

理想情况下，我希望在运行的脚本中自动执行此操作

例如，假设我有去年的月度数据，我预测了一年

library(forecast)
StartingData <- (1:12)+rnorm(1:12)
forecast(ts(StartingData,start=c(2011,1), frequency =12),h=12)

库（预测）
开始数据这是一种方法。如果您希望新旧数据并排出现，则可以重铸数据

library(forecast)
StartingData <- (1:12)+rnorm(1:12)
d1=data.frame(forecast(ts(StartingData,start=c(2011,1), frequency =12),h=12))
d1$times=row.names(d1)
d1$fcast='old'

StartingData[13:15] <- 10
d2=data.frame(forecast(ts(StartingData,start=c(2011,1), frequency =12),h=12))
d2$times=row.names(d2)
d2$fcast='new'

combined=rbind(d1,d2)
row.names(combined)=NULL

combined

> combined
   Point.Forecast     Lo.80    Hi.80     Lo.95    Hi.95    times fcast
1        12.58567 11.652976 13.51837 11.159237 14.01211 Jan 2012   old
2        13.53736 12.604661 14.47005 12.110921 14.96379 Feb 2012   old
3        14.48904 13.556345 15.42174 13.062605 15.91548 Mar 2012   old
4        15.44073 14.508029 16.37342 14.014289 16.86716 Apr 2012   old
5        16.39241 15.459713 17.32511 14.965973 17.81885 May 2012   old
6        17.34409 16.411397 18.27679 15.917657 18.77053 Jun 2012   old
7        18.29578 17.363081 19.22848 16.869341 19.72222 Jul 2012   old
8        19.24746 18.314765 20.18016 17.821024 20.67390 Aug 2012   old
9        20.19915 19.266449 21.13185 18.772708 21.62559 Sep 2012   old
10       21.15083 20.218133 22.08353 19.724391 22.57727 Oct 2012   old
11       22.10252 21.169816 23.03522 20.676075 23.52896 Nov 2012   old
12       23.05420 22.121500 23.98690 21.627758 24.48064 Dec 2012   old
13       11.06443  8.716179 13.41269  7.473087 14.65578 Apr 2012   new
14       11.33021  8.925497 13.73491  7.652521 15.00789 May 2012   new
15       11.56613  9.111298 14.02095  7.811791 15.32046 Jun 2012   new
16       11.77555  9.276224 14.27488  7.953161 15.59794 Jul 2012   new
17       11.96145  9.422619 14.50028  8.078643 15.84426 Aug 2012   new
18       12.12647  9.552565 14.70038  8.190020 16.06293 Sep 2012   new
19       12.27296  9.667908 14.87802  8.288876 16.25705 Oct 2012   new
20       12.40300  9.770290 15.03571  8.376618 16.42938 Nov 2012   new
21       12.51843  9.861164 15.17569  8.454494 16.58236 Dec 2012   new
22       12.62089  9.941825 15.29996  8.523612 16.71817 Jan 2013   new
23       12.71185 10.013418 15.41028  8.584955 16.83874 Feb 2013   new
24       12.79259 10.076963 15.50822  8.639396 16.94579 Mar 2013   new
> 

库（预测）
启动数据
这是一种方法。如果您希望新旧数据并排出现，则可以重铸数据

library(forecast)
StartingData <- (1:12)+rnorm(1:12)
d1=data.frame(forecast(ts(StartingData,start=c(2011,1), frequency =12),h=12))
d1$times=row.names(d1)
d1$fcast='old'

StartingData[13:15] <- 10
d2=data.frame(forecast(ts(StartingData,start=c(2011,1), frequency =12),h=12))
d2$times=row.names(d2)
d2$fcast='new'

combined=rbind(d1,d2)
row.names(combined)=NULL

combined

> combined
   Point.Forecast     Lo.80    Hi.80     Lo.95    Hi.95    times fcast
1        12.58567 11.652976 13.51837 11.159237 14.01211 Jan 2012   old
2        13.53736 12.604661 14.47005 12.110921 14.96379 Feb 2012   old
3        14.48904 13.556345 15.42174 13.062605 15.91548 Mar 2012   old
4        15.44073 14.508029 16.37342 14.014289 16.86716 Apr 2012   old
5        16.39241 15.459713 17.32511 14.965973 17.81885 May 2012   old
6        17.34409 16.411397 18.27679 15.917657 18.77053 Jun 2012   old
7        18.29578 17.363081 19.22848 16.869341 19.72222 Jul 2012   old
8        19.24746 18.314765 20.18016 17.821024 20.67390 Aug 2012   old
9        20.19915 19.266449 21.13185 18.772708 21.62559 Sep 2012   old
10       21.15083 20.218133 22.08353 19.724391 22.57727 Oct 2012   old
11       22.10252 21.169816 23.03522 20.676075 23.52896 Nov 2012   old
12       23.05420 22.121500 23.98690 21.627758 24.48064 Dec 2012   old
13       11.06443  8.716179 13.41269  7.473087 14.65578 Apr 2012   new
14       11.33021  8.925497 13.73491  7.652521 15.00789 May 2012   new
15       11.56613  9.111298 14.02095  7.811791 15.32046 Jun 2012   new
16       11.77555  9.276224 14.27488  7.953161 15.59794 Jul 2012   new
17       11.96145  9.422619 14.50028  8.078643 15.84426 Aug 2012   new
18       12.12647  9.552565 14.70038  8.190020 16.06293 Sep 2012   new
19       12.27296  9.667908 14.87802  8.288876 16.25705 Oct 2012   new
20       12.40300  9.770290 15.03571  8.376618 16.42938 Nov 2012   new
21       12.51843  9.861164 15.17569  8.454494 16.58236 Dec 2012   new
22       12.62089  9.941825 15.29996  8.523612 16.71817 Jan 2013   new
23       12.71185 10.013418 15.41028  8.584955 16.83874 Feb 2013   new
24       12.79259 10.076963 15.50822  8.639396 16.94579 Mar 2013   new
> 

库（预测）
启动数据
简单地回答您的问题，您希望访问这两组数据以进行进一步比较，对吗？是的-我正在尝试找到自动计算这两组数据的最佳方法。它提供了一个与预测相关的日期，这一事实使我认为我应该能够在该日期之前将两个数据集绑定在一起。但是，我找不到实现这一点的对象。简单地说，你想访问这两组数据进行进一步比较，对吗？是的，我正在尝试找到最好的方法来自动计算这两组数据。它提供了一个与预测相关的日期，这一事实使我认为我应该能够在该日期之前将两个数据集绑定在一起。但是，我找不到实现这一点的对象。当然，我想补充的是，您可以将日期转换为日期字段，但我现在没有考虑。当然，我想补充的是，您可以将日期转换为日期字段，但我现在没有考虑。