Haroon Rashid -3 years ago 53
R Question

# Handle Continous Missing values in time-series data

I have a time-series data as shown below.

``````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
``````

How should I handle Continous NA values. In cases where I have only one NA, I use to take the average of extreme values of NA entry. Are there any standard approaches to deal with continuous missing values?

The `zoo` package has several functions for dealing with `NA` values. One of the following functions might suit your needs:

• `na.locf`: Last observation carried forward. Using the parameter `fromLast = TRUE` corresponds to next observation carried backward (NOCB).
• `na.aggregate`: Replace the `NA`'s with some aggregated value. The default aggregation function is the `mean`, but you can specify other functions as well. See `?na.aggregate` for more info.
• `na.approx`: `NA`'s are replaced with linear interpolated values.

You can compare the outcomes to see what these functions do:

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

this results in:

``````> 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
``````

Used data:

``````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")
``````
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