Is it possible to merge two time series in one?

Question

I've been trying to merge two ts objects, the second one starts exactly one period after the next one. For example, take the following two time series

ts1<-ts(c(1:12),star=c(2014,1),freq=12)
ts2<-ts(c(13:24),star=c(2015,1),freq=12)

As you can see, both of them match perfectly in order to make a single ts out of this two ts objects. I thought the logical answer would be the rbind() function. But it makes a matrix out of them, as follows...

> rbind(ts1,ts2)
    [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
ts1    1    2    3    4    5    6    7    8    9    10    11    12
ts2   13   14   15   16   17   18   19   20   21    22    23    24

I've tried without success other functions like merge,cbind. Using c() I've managed to get one only timeseries, the main problema is that I lose structure attributes of the original timeseries, this is bad because I'm trying to use the function forecast with the new ts but it gives me this:

Error: variables ... were specified with different types from the fit

I would be happy just with being able to add additional observations to a time series. Something like adding the value 13 to ts1 for January,2015 but I haven´t found how to do this either.

I think is funny because I see this as a perfectly natural thing to ask for to a ts object, but I haven´t found any other question that helps me out here. Well let´s hope this is not a too silly question.

Answer 1

You need to reassign the attributes from the first series.

> ts(c(ts1,ts2), start=start(ts1), frequency=frequency(ts1))
     Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2014   1   2   3   4   5   6   7   8   9  10  11  12
2015  13  14  15  16  17  18  19  20  21  22  23  24

Answer 2

This worked for me

ts.union(ts1, ts2)
             ts1 ts2
    Jan 2014   1  NA
    Feb 2014   2  NA
    Mar 2014   3  NA
    Apr 2014   4  NA
    May 2014   5  NA
    Jun 2014   6  NA
    Jul 2014   7  NA
    Aug 2014   8  NA
    Sep 2014   9  NA
    Oct 2014  10  NA
    Nov 2014  11  NA
    Dec 2014  12  NA
    Jan 2015  NA  13
    Feb 2015  NA  14
    Mar 2015  NA  15
    Apr 2015  NA  16
    May 2015  NA  17
    Jun 2015  NA  18
    Jul 2015  NA  19
    Aug 2015  NA  20
    Sep 2015  NA  21
    Oct 2015  NA  22
    Nov 2015  NA  23
    Dec 2015  NA  24