Weighted Moving Average based on Irregular Date Intervals - r

Question

I am new to time series and was hoping someone could provide some input/ideas here.
I am trying to find ways to impute missing values.
I was hoping to find the moving average, but most of the packages (smooth, mgcv, etc.) don't seem to take time intervals into consideration.
For example, the dataset might look like something below and I would want value at 2016-01-10 to have the greatest influence in calculating the missing value:
Date Value Diff_Days
2016-01-01 10 13
2016-01-10 14 4
2016-01-14 NA 0
2016-01-28 30 14
2016-01-30 50 16
I have instances where NA might be the first observation or the last observation. Sometimes NA values also occur multiple times, at which point the rolling window would need to expand, and this is why I would like to use the moving average.
Is there a package that would take date intervals / separate weights into consideration?
Or please suggest if there is a better way to impute NA values in such cases.

Answer 1

You can use glm or any different model.
Input
con <- textConnection("Date Value Diff_Days
2015-12-14 NA 0
2016-01-01 10 13
2016-01-10 14 4
2016-01-14 NA 0
2016-01-28 30 14
2016-02-14 NA 0
2016-02-18 NA 0
2016-02-29 50 16")
df <- read.table(con, header = T)
df$Date <- as.Date(df$Date)
df$Date.numeric <- as.numeric(df$Date)
fit <- glm(Value ~ Date.numeric, data = df)
df.na <- df[is.na(df$Value),]
predicted <- predict(fit, df.na)
df$Value[is.na(df$Value)] <- predicted
plot(df$Date, df$Value)
points(df.na$Date, predicted, type = "p", col="red")
df$Date.numeric <- NULL
rm(df.na)
print(df)
Output
Date Value Diff_Days
1 2015-12-14 -3.054184 0
2 2016-01-01 10.000000 13
3 2016-01-10 14.000000 4
4 2016-01-14 18.518983 0
5 2016-01-28 30.000000 14
6 2016-02-14 40.092149 0
7 2016-02-18 42.875783 0
8 2016-02-29 50.000000 16