Getting repeated results in SVM in R

I'm trying to predict using SVM from R package "e1071". But I'm getting repeated values as predicted outcomes. I have run this several times but getting same results. Please help me to find what wrong is going here.

library(e1071)
tuneResult <- tune(svm,y~.,data=calibration.data,ranges = list(epsilon = seq(0,1,0.1), cost = 2^(2:9)))
tunedModel <- tuneResult$best.model
predict.data<-predict(tunedModel,prediction.data)
predict.data


2006-03-01 2006-04-01 2006-05-01 2006-06-01 2006-07-01 2006-08-01 2006-09-01 2006-10-01 2006-11-01 2006-12-01 2007-01-01 2007-02-01 2007-03-01 2007-04-01 2007-05-01
0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676
2007-06-01 2007-07-01 2007-08-01 2007-09-01 2007-10-01 2007-11-01 2007-12-01 2008-01-01 2008-02-01 2008-03-01 2008-04-01 2008-05-01 2008-06-01 2008-07-01 2008-08-01
0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676
2008-09-01 2008-10-01 2008-11-01 2008-12-01 2009-01-01 2009-02-01 2009-03-01 2009-04-01 2009-05-01 2009-06-01 2009-07-01 2009-08-01 2009-09-01 2009-10-01 2009-11-01
0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676 0.05798676

.....and so on.


Calibration data is in the following format:

y X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12
1991-03-01 -8.5371 -0.4166 -0.1033 -0.2974 0.0130 -3.7858 -3.6329 -4.0668 -3.9070 -5.6851 -5.7326 -6.2762 -6.3069
1991-04-01 -8.4224 2.2427 2.6264 2.5721 2.9529 -0.4166 -0.1033 -0.2974 0.0130 -3.7858 -3.6329 -4.0668 -3.9070
1991-05-01 -8.1701 4.0619 4.4961 4.4715 4.9228 2.2427 2.6264 2.5721 2.9529 -0.4166 -0.1033 -0.2974 0.0130
1991-06-01 -8.5097 3.6112 3.6158 4.0910 4.0928 4.0619 4.4961 4.4715 4.9228 2.2427 2.6264 2.5721 2.9529
1991-07-01 -8.5450 0.8204 0.6754 1.1004 0.9026 3.6112 3.6158 4.0910 4.0928 4.0619 4.4961 4.4715 4.9228
1991-08-01 -4.3212 0.2197 0.1151 0.2999 0.1626 0.8204 0.6754 1.1004 0.9026 3.6112 3.6158 4.0910 4.0928
1991-09-01 1.0677 0.8090 0.8248 0.8393 0.8325 0.2197 0.1151 0.2999 0.1626 0.8204 0.6754 1.1004 0.9026
1991-10-01 2.4652 1.1682 1.0744 1.0487 0.9724 0.8090 0.8248 0.8393 0.8325 0.2197 0.1151 0.2999 0.1626
1991-11-01 -1.5196 -0.4426 -0.7859 -0.8218 -1.1477 1.1682 1.0744 1.0487 0.9724 0.8090 0.8248 0.8393 0.8325
1991-12-01 -6.7485 -3.8733 -4.2662 -4.3024 -4.6878 -0.4426 -0.7859 -0.8218 -1.1477 1.1682 1.0744 1.0487 0.9724
1992-01-01 -7.8150 -4.9241 -4.9465 -5.5029 -5.5178 -3.8733 -4.2662 -4.3024 -4.6878 -0.4426 -0.7859 -0.8218 -1.1477
1992-02-01 -5.7313 -4.4448 -4.4869 -4.7235 -4.7979 -4.9241 -4.9465 -5.5029 -5.5178 -3.8733 -4.2662 -4.3024 -4.6878
1992-03-01 -0.9963 -0.6056 -0.2872 -0.5941 -0.2780 -4.4448 -4.4869 -4.7235 -4.7979 -4.9241 -4.9465 -5.5029 -5.5178
....................
2005-11-01 -4.2856 -1.8385 -2.3010 -2.0255 -2.5012 0.8122 0.5494 0.8150 0.5389 0.2630 0.2697 0.3856 0.3990
2005-12-01 -6.3069 -4.8993 -5.1313 -5.4161 -5.6413 -1.8385 -2.3010 -2.0255 -2.5012 0.8122 0.5494 0.8150 0.5389


Prediction data is :

X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12
2006-03-01 25.47954 25.12117 25.32201 25.01688 21.95794 21.53203 21.73904 21.41161 19.60088 19.29948 19.04211 18.89265
2006-04-01 30.07153 30.01922 30.53638 30.34532 25.47954 25.12117 25.32201 25.01688 21.95794 21.53203 21.73904 21.41161
2006-05-01 32.17442 32.34361 33.43570 33.57129 30.07153 30.01922 30.53638 30.34532 25.47954 25.12117 25.32201 25.01688
2006-06-01 28.61485 28.59253 29.06019 29.09922 32.17442 32.34361 33.43570 33.57129 30.07153 30.01922 30.53638 30.34532
2006-07-01 28.13924 27.78606 28.53410 28.16312 28.61485 28.59253 29.06019 29.09922 32.17442 32.34361 33.43570 33.57129


Please help me to find out a solution.

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