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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