R 扩展时间序列_R_Timestamp_Time Series

R 扩展时间序列

R 扩展时间序列,r,timestamp,time-series,R,Timestamp,Time Series,我最初有15分钟间隔的流量数据，看起来像这样 structure(list(t = structure(c(1136062800, 1136063700, 1136064600, 1136065500, 1136066400, 1136067300, 1136068200, 1136069100, 1136070000, 1136070900, 1136071800, 1136072700, 1136073600, 1136074500, 1136075400, 1136076300, 1

我最初有15分钟间隔的流量数据，看起来像这样

structure(list(t = structure(c(1136062800, 1136063700, 1136064600, 
1136065500, 1136066400, 1136067300, 1136068200, 1136069100, 1136070000, 
1136070900, 1136071800, 1136072700, 1136073600, 1136074500, 1136075400, 
1136076300, 1136077200, 1136078100, 1136079000, 1136079900, 1136080800, 
1136081700, 1136082600, 1136083500, 1136084400, 1136085300, 1136086200, 
1136087100, 1136088000, 1136088900, 1136089800, 1136090700), class = c("POSIXct", 
"POSIXt"), tzone = "EST"), flow = c(23, 31, 42, 59, 59, 59, 50, 
48, 37, 33, 31, 31, 30, 30, 27, 27, 30, 31, 33, 37, 38, 42, 42, 
48, 48, 46, 42, 38, 37, 35, 33, 35)), .Names = c("t", "flow"), row.names = 35003:35034, class = "data.frame")

我使用以下代码将这些数据切割成平均每小时2次的数据时间序列

data您可以通过使用函数approxfun实现此结果

首先，让我们将SmoothdData中的时间列转换回POSIX时间格式：

smoothedData$time <- as.POSIXct(smoothedData$time)
#                  time   flow
# 1 2005-12-31 16:00:00 46.375
# 2 2005-12-31 18:00:00 30.750
# 3 2005-12-31 20:00:00 37.625
# 4 2005-12-31 22:00:00 39.250

最后，将此函数应用于以小时为间隔的时间序列：

data1hr <- data.frame(time=seq(min(smoothedData$time),max(smoothedData$time),by='1 hour'))

data1hr$flow <- interp.time(data1hr$time)
#                  time    flow
# 1 2005-12-31 16:00:00 46.3750
# 2 2005-12-31 17:00:00 38.5625
# 3 2005-12-31 18:00:00 30.7500
# 4 2005-12-31 19:00:00 34.1875
# 5 2005-12-31 20:00:00 37.6250
# 6 2005-12-31 21:00:00 38.4375
# 7 2005-12-31 22:00:00 39.2500

编辑

根据OP的评论

我觉得我解释我的问题的方式比它更复杂成为。基本上，在偶数小时的时间戳，即00:00:00， 02:00:00我想要2小时平均值，但时间戳为奇数小时 01:00:00，03:00:00我要每小时平均值

我想为这个问题提出另一个解决方案：

data <- structure(list(t = structure(c(1136062800, 1136063700, 1136064600,
1136065500, 1136066400, 1136067300, 1136068200, 1136069100, 1136070000,
1136070900, 1136071800, 1136072700, 1136073600, 1136074500, 1136075400,
1136076300, 1136077200, 1136078100, 1136079000, 1136079900, 1136080800,
1136081700, 1136082600, 1136083500, 1136084400, 1136085300, 1136086200,
1136087100, 1136088000, 1136088900, 1136089800, 1136090700), class = c("POSIXct",
"POSIXt"), tzone = "EST"), flow = c(23, 31, 42, 59, 59, 59, 50,
48, 37, 33, 31, 31, 30, 30, 27, 27, 30, 31, 33, 37, 38, 42, 42,
48, 48, 46, 42, 38, 37, 35, 33, 35)), .Names = c("t", "flow"), row.names = 35003:35034, class = "data.frame")

data2hr <- data
data2hr$time <- cut(data2hr$t,breaks="2 hours")
smoothedData2hr <- aggregate(flow~time,data2hr,mean)
#                 time   flow
# 1 2005-12-31 16:00:00 46.375
# 2 2005-12-31 18:00:00 30.750
# 3 2005-12-31 20:00:00 37.625
# 4 2005-12-31 22:00:00 39.250

data1hr <- data
data1hr$time <- cut(data1hr$t,breaks="1 hour")
smoothedData1hr <- aggregate(flow~time,data1hr,mean)
#                  time  flow
# 1 2005-12-31 16:00:00 38.75
# 2 2005-12-31 17:00:00 54.00
# 3 2005-12-31 18:00:00 33.00
# 4 2005-12-31 19:00:00 28.50
# 5 2005-12-31 20:00:00 32.75
# 6 2005-12-31 21:00:00 42.50
# 7 2005-12-31 22:00:00 43.50
# 8 2005-12-31 23:00:00 35.00

result <- smoothedData1hr
result$flow[match(smoothedData2hr$time,result$time)] <- smoothedData2hr$flow
#                  time   flow
# 1 2005-12-31 16:00:00 46.375
# 2 2005-12-31 17:00:00 54.000
# 3 2005-12-31 18:00:00 30.750
# 4 2005-12-31 19:00:00 28.500
# 5 2005-12-31 20:00:00 37.625
# 6 2005-12-31 21:00:00 42.500
# 7 2005-12-31 22:00:00 39.250
# 8 2005-12-31 23:00:00 35.000

我不想要插值数据，因为我在原始数据中实际有该时间步的数据，所以我想要取2小时时间序列中缺少的每小时时间步的平均值，然后合并它们！为什么不直接使用data2hr$时间呢？我觉得我用了一种比实际更复杂的方式解释了我的问题。基本上，在偶数小时时间戳，即00:00:00，02:00:00，我想要2小时平均值，但在奇数小时时间戳01:00:00，03:00:00，我想要小时平均值。然后，您可以生成一个平均一小时的数据集，例如SmoothDData1h，并用您已有的SmoothDData数据集中的匹配记录替换其中的偶数小时记录。没错，这正是我试图做的，但似乎找不到方法！

data <- structure(list(t = structure(c(1136062800, 1136063700, 1136064600,
1136065500, 1136066400, 1136067300, 1136068200, 1136069100, 1136070000,
1136070900, 1136071800, 1136072700, 1136073600, 1136074500, 1136075400,
1136076300, 1136077200, 1136078100, 1136079000, 1136079900, 1136080800,
1136081700, 1136082600, 1136083500, 1136084400, 1136085300, 1136086200,
1136087100, 1136088000, 1136088900, 1136089800, 1136090700), class = c("POSIXct",
"POSIXt"), tzone = "EST"), flow = c(23, 31, 42, 59, 59, 59, 50,
48, 37, 33, 31, 31, 30, 30, 27, 27, 30, 31, 33, 37, 38, 42, 42,
48, 48, 46, 42, 38, 37, 35, 33, 35)), .Names = c("t", "flow"), row.names = 35003:35034, class = "data.frame")

data2hr <- data
data2hr$time <- cut(data2hr$t,breaks="2 hours")
smoothedData2hr <- aggregate(flow~time,data2hr,mean)
#                 time   flow
# 1 2005-12-31 16:00:00 46.375
# 2 2005-12-31 18:00:00 30.750
# 3 2005-12-31 20:00:00 37.625
# 4 2005-12-31 22:00:00 39.250

data1hr <- data
data1hr$time <- cut(data1hr$t,breaks="1 hour")
smoothedData1hr <- aggregate(flow~time,data1hr,mean)
#                  time  flow
# 1 2005-12-31 16:00:00 38.75
# 2 2005-12-31 17:00:00 54.00
# 3 2005-12-31 18:00:00 33.00
# 4 2005-12-31 19:00:00 28.50
# 5 2005-12-31 20:00:00 32.75
# 6 2005-12-31 21:00:00 42.50
# 7 2005-12-31 22:00:00 43.50
# 8 2005-12-31 23:00:00 35.00

result <- smoothedData1hr
result$flow[match(smoothedData2hr$time,result$time)] <- smoothedData2hr$flow
#                  time   flow
# 1 2005-12-31 16:00:00 46.375
# 2 2005-12-31 17:00:00 54.000
# 3 2005-12-31 18:00:00 30.750
# 4 2005-12-31 19:00:00 28.500
# 5 2005-12-31 20:00:00 37.625
# 6 2005-12-31 21:00:00 42.500
# 7 2005-12-31 22:00:00 39.250
# 8 2005-12-31 23:00:00 35.000