滚动窗口在不规则的时间序列

Question

我有一个不规则的时间序列事件(帖子)使用xts,我想计算在滚动的每周窗口(或每两周,或3天等)发生的事件的数量.数据如下所示:

                    postid
2010-08-04 22:28:07    867
2010-08-04 23:31:12    891
2010-08-04 23:58:05    901
2010-08-05 08:35:50    991
2010-08-05 13:28:02   1085
2010-08-05 14:14:47   1114
2010-08-05 14:21:46   1117
2010-08-05 15:46:24   1151
2010-08-05 16:25:29   1174
2010-08-05 23:19:29   1268
2010-08-06 12:15:42   1384
2010-08-06 15:22:06   1403
2010-08-07 10:25:49   1550
2010-08-07 18:58:16   1596
2010-08-07 21:15:44   1608

应该产生类似的东西

                    nposts
2010-08-05 00:00:00     10
2010-08-06 00:00:00      9
2010-08-07 00:00:00      5

为期2天的窗口.我已研究过rollapply,apply.rolling从PerformanceAnalytics等,他们都承担一定的时间序列数据.我尝试将所有时间都改变到帖子发生的那一天,并使用类似于ddply每天分组的东西,这让我很接近.但是,用户可能不会每天发布,因此时间序列仍然是不规则的.我可以用0填补空白,但这可能会使我的数据大量膨胀,而且已经非常大了.

我该怎么办？

Answer 1

这是使用xts的解决方案:

x <- structure(c(867L, 891L, 901L, 991L, 1085L, 1114L, 1117L, 1151L, 
  1174L, 1268L, 1384L, 1403L, 1550L, 1596L, 1608L), .Dim = c(15L, 1L),
  index = structure(c(1280960887, 1280964672, 1280966285, 
  1280997350, 1281014882, 1281017687, 1281018106, 1281023184, 1281025529, 
  1281050369, 1281096942, 1281108126, 1281176749, 1281207496, 1281215744),
  tzone = "", tclass = c("POSIXct", "POSIXt")), class = c("xts", "zoo"),
  .indexCLASS = c("POSIXct", "POSIXt"), tclass = c("POSIXct", "POSIXt"),
  .indexTZ = "", tzone = "")
# first count the number of observations each day
xd <- apply.daily(x, length)
# now sum the counts over a 2-day rolling window
x2d <- rollapply(xd, 2, sum)
# align times at the end of the period (if you want)
y <- align.time(x2d, n=60*60*24)  # n is in seconds

Answer 2

这似乎有效：

# n = number of days
n <- 30
# w = window width. In this example, w = 7 days
w <- 7

# I will simulate some data to illustrate the procedure
data <- rep(1:n, rpois(n, 2))

# Tabulate the number of occurences per day:
# (use factor() to be sure to have the days with zero observations included)
date.table <- table(factor(data, levels=1:n))  

mat <- diag(n)
for (i in 2:w){
  dim <- n+i-1
  mat <- mat + diag(dim)[-((n+1):dim),-(1:(i-1))]
  }

# And the answer is.... 
roll.mean.7days <- date.table %*% mat

似乎并不太慢（尽管mat矩阵将获得维度 n*n）。我尝试将 n=30 替换为 n=3000（这会创建一个包含 900 万个元素 = 72 MB 的矩阵），但在我的计算机上它仍然相当快。对于非常大的数据集，首先尝试子集......使用 Matrix 包（bandSparse）中的一些函数来创建矩阵也会更快mat。