I have the following time series

`1.3578511
0.5119648
1.3189847
0.9214787
1.2272616
4.9167998
1.2272616
1.2272616
0.8854192
2.3386331
1.6132899
0.2030302
0.8426226
1.2277843
NA
1.3189847
1.3578511
0.8530141
2.3386331
1.0541099
0.7747481
0.5764672
1.3189847
1.2160533
1.2272616
0.6715839
0.9651803
1.6132899
1.2006974
0.6875047
1.3245534
1.2006974
0.8221709
1.3101684
1.6132899
1.6132899
1.2006974
1.3189847
1.0018480
1.2277843
1.4424190
1.6132899
1.2277843
1.2006974
0.7779642
0.9381081
0.8854192
NA
NA
1.3189847
1.1070461
0.8221709
4.9167998
0.9214787
1.3189847
1.3189847
1.2277843
1.4424190
1.6132899
1.6132899
4.9167998
0.8235792
0.9708839
1.1070461
1.2160533
0.8354292
1.4424190
1.1958634
0.5119648
1.4424190
1.4424190
1.6132899
1.6132899
0.6710844
1.2272616
0.9708839
0.8890464
1.4424190
0.8890464
0.8221709
1.1958634
0.8132233
0.4630722
4.9167998
0.8890464
1.3189847
0.7373181
1.1070461
1.2279813
0.8890464
0.3588158
1.4424190
0.8132233
0.4297043
1.3578511
4.9167998
1.2272616
0.8426226
1.4424190
1.6132899
NA
` 

in which `NA` are missing values, and I want to predict/forecast it. I searched over internet, but I haven't found that the [Amelia][1] package can impute missing values.

I used it as follows: 

    library(Amelia)
    t <- read.table("C:\\Users\\exam\\Desktop\\missing_ts.txt")
    a.out <- amelia(t)

but I got the following error:

    Amelia Error Code:  42 
    There is only 1 column of data. Cannot impute

Likewise,  

    amelia(x=as.matrix(1:101,t$V1))

resulted in

    Amelia Error Code:  39 
    Your data has no missing values.  Make sure the code for 
    missing data is set to the code for R, which is NA.

Is there something wrong in the way I'm trying to forecast this time series? If yes, then what method should I use?
 


  [1]: http://cran.r-project.org/web/packages/Amelia/index.html