how to predict/forecast missing values in time series ?

i have time series as
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,i want to predict/forecast it,i search
on
internet,i found that Amelia packages can impute missing values;
i used but it giving error,how can i resolve it

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

a.out <- amelia(t)

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

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

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

amelia(t$V1)

Error in colSums(!is.na(x)) : 
 'x' must be an array of at least two dimensions

is my way of predicting wrong?,if yes,then which method should i
follow?





















































































































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library (zoo)
?na.approx

Note that you need to define an index (time base) to go along with your data, but that could be as simple as a sequence of integers.