处理时间序列数据中的连续缺失值

时间:2015-09-21 11:40:22

标签: r time-series na

我有一个时间序列数据,如下所示。

2015-04-26 23:00:00  5704.27388916015661380
2015-04-27 00:00:00  4470.30868326822928793
2015-04-27 01:00:00  4552.57241617838553793
2015-04-27 02:00:00  4570.22250032825650123
2015-04-27 03:00:00  NA
2015-04-27 04:00:00  NA
2015-04-27 05:00:00  NA
2015-04-27 06:00:00 12697.37724086216439900
2015-04-27 07:00:00  5538.71119009653739340
2015-04-27 08:00:00    81.95060647328695325
2015-04-27 09:00:00  8550.65816895300667966
2015-04-27 10:00:00  2925.76573206583680076

我应该如何处理Continous NA值。在我只有一个NA的情况下,我用来取NA条目的极值的平均值。是否有任何标准方法来处理连续缺失值?

1 个答案:

答案 0 :(得分:10)

zoo包有几个用于处理NA值的函数。以下功能之一可能适合您的需求:

  • na.locf:最后的观察结果。使用参数fromLast = TRUE对应于后向观察(NOCB)。
  • na.aggregate:将NA替换为某些聚合值。默认聚合函数是mean,但您也可以指定其他函数。有关详细信息,请参阅?na.aggregate
  • na.approxNA被替换为线性插值。

您可以比较结果以了解这些功能的作用:

library(zoo)
df$V.loc <- na.locf(df$V2)
df$V.agg <- na.aggregate(df$V2)
df$V.app <- na.approx(df$V2)

这导致:

> df
                    V1          V2       V.loc       V.agg       V.app
1  2015-04-26 23:00:00  5704.27389  5704.27389  5704.27389  5704.27389
2  2015-04-27 00:00:00  4470.30868  4470.30868  4470.30868  4470.30868
3  2015-04-27 01:00:00  4552.57242  4552.57242  4552.57242  4552.57242
4  2015-04-27 02:00:00  4570.22250  4570.22250  4570.22250  4570.22250
5  2015-04-27 03:00:00          NA  4570.22250  5454.64894  6602.01119
6  2015-04-27 04:00:00          NA  4570.22250  5454.64894  8633.79987
7  2015-04-27 05:00:00          NA  4570.22250  5454.64894 10665.58856
8  2015-04-27 06:00:00 12697.37724 12697.37724 12697.37724 12697.37724
9  2015-04-27 07:00:00  5538.71119  5538.71119  5538.71119  5538.71119
10 2015-04-27 08:00:00    81.95061    81.95061    81.95061    81.95061
11 2015-04-27 09:00:00  8550.65817  8550.65817  8550.65817  8550.65817
12 2015-04-27 10:00:00  2925.76573  2925.76573  2925.76573  2925.76573

使用过的数据:

df <- structure(list(V1 = structure(c(1430082000, 1430085600, 1430089200, 1430092800, 1430096400, 1430100000, 1430103600, 1430107200, 1430110800, 1430114400, 1430118000, 1430121600), class = c("POSIXct", "POSIXt"), tzone = ""), V2 = c(5704.27388916016, 4470.30868326823, 4552.57241617839, 4570.22250032826, NA, NA, NA, 12697.3772408622, 5538.71119009654, 81.950606473287, 8550.65816895301, 2925.76573206584)), .Names = c("V1", "V2"), row.names = c(NA, -12L), class = "data.frame")

增加:

还有其他时间序列函数用于处理imputeTSforecast包中的NA(还有一些更高级的函数)。

例如:

 library("imputeTS")

 # Moving Average Imputation
 na.ma(df$V2)

 # Imputation via Kalman Smoothing on structural time series models 
 na.kalman(df$V2)

 # Just interpolation but with some nice options (linear, spline,stine)
 na.interpolation(df$V2)

library("forecast")

#Interpolation via seasonal decomposition and interpolation
na.interp(df$V2)