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

`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. `

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