dealing with NA in seasonal cycle analysis R

I have a timeseries of monthly data with lots of missing datapoints, set to NA. I want to simply subtract the annual cycle from the data, ignoring the missing entries. It seems that the decompose function can't handle missing data points, but I have seen elsewhere that the seasonal package is suggested instead. However I am also running into problems there too with the NA.

Here is a minimum reproducible example of the problem using a built in dataset...

library(seasonal)  # set range to missing NA in Co2 dataset c2<-co2 c2[c2>330 & c2<350]=NA seas(c2,na.action=na.omit)  Error in na.omit.ts(x) : time series contains internal NAs 

Yes, I know! that's why I asked you to omit them! Let's try this:

seas(c2,na.action=na.x13)  Error: X-13 run failed  Errors: - Adding MV1981.Apr exceeds the number of regression effects   allowed in the model (80). 

Hmmm, interesting, no idea what that means, okay, please just exclude the NA:

seas(c2,na.action=na.exclude)  Error in na.omit.ts(x) : time series contains internal NAs 

that didn't help much! and for good measure

decompose(c2)  Error in na.omit.ts(x) : time series contains internal NAs 

I'm on the following:

R version 3.4.4 (2018-03-15) -- "Someone to Lean On" Copyright (C) 2018 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 

Why is leaving out NA such a problem? I'm obviously being completely stupid, but I can't see what I'm doing wrong with the seas function. Happy to consider an alternative solution using xts.

1 Answers

Answers 1

My first solution, simply manually calculating the seasonal cycle, converting to a dataframe to subtract the vector and then transforming back.

# seasonal cycle scycle=tapply(c2,cycle(c2),mean,na.rm=T)  # converting to df df=tapply(c2, list(year=floor(time(c2)), month = cycle(c2)), c) # subtract seasonal cycle for (i in 1:nrow(df)){df[i,]=df[i,]-scycle} # convert back to timeseries anomco2=ts(c(t(df)),start=start(c2),freq=12) 

Not very pretty, and not very efficient either.

The comment of missuse lead me to another Seasonal decompose of monthly data including NA in r I missed with a near duplicate question and this suggested the package zoo, which seems to work really well for additive series

library(zoo) c2=co2 c2[c2>330&c2<350]=NA d=decompose(na.StructTS(ts))  plot(c2) lines(d$x,col="red") 

shows that the series is very well reconstructed through the missing period.

The output of deconstruct has the trend and seasonal cycle available. I wish I could transfer my bounty to user https://stackoverflow.com/users/516548/g-grothendieck for this helpful response. Thanks to user missuse too.

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