SVM模型预测

library(e1071)
source <- c(10930,10318,10595,10972,7706,6756,9092,10551,9722,10913,11151,8186,6422,
6337,11649,11652,10310,12043,7937,6476,9662,9570,9981,9331,9449,6773,6304,9355,10477,
10148,10395,11261,8713,7299,10424,10795,11069,11602,11427,9095,7707,10767,12136,12812,
12006,12528,10329,7818,11719,11683,12603,11495,13670,11337,10232,13261,13230,15535,
16837,19598,14823,11622,19391,18177,19994,14723,15694,13248,9543,12872,13101,15053,
12619,13749,10228,9725,14729,12518,14564,15085,14722,11999,9390,13481,14795,15845,
15271,14686,11054,10395,14775,14618,16029,15231,14246,12095,10473,15323,15381,14947)
srcLen<-length(source)
for(i in 1:10){
real <- source[srcLen-i+1]
xNum=(srcLen-i+1)%/%7
yNum=7
data<-array(1:(xNum*yNum),c(xNum,yNum))

pre=srcLen-i+1;
for(x in 1:xNum){
for(y in 1:yNum){
data[x,y]=source[pre]
pre=pre-1;
}
if(pre<7){
break;
}
}
ascData<-array(1:(xNum*yNum),c(xNum,yNum))
for(x in 1:xNum){
for(y in 1:yNum){
ascData[x,y]=data[xNum-x+1,y]
}
}
colnames(ascData) <- c("a","b","c","d","e","f","g")

trainData<-data.frame(ascData)

nn<-svm(a~b+c+d+e+f+g,data=trainData[1:(xNum-1),])

predict<-predict(nn,trainData[xNum,])

percent <- (predict-real)*100/real

res <- paste("预测值：",predict,"实际值：",real,"误差：",percent)
print(res)
}

[1] "预测值： 15903.4179393 实际值： 14947 误差： 6.3987284358066"
[1] "预测值： 14512.9521578654 实际值： 15381 误差： -5.64363722862387"
[1] "预测值： 14385.6991495925 实际值： 15323 误差： -6.1169539281307"
[1] "预测值： 10110.2340088228 实际值： 10473 误差： -3.46382117041125"
[1] "预测值： 11455.4950069718 实际值： 12095 误差： -5.28735008704629"
[1] "预测值： 15189.6013117541 实际值： 14246 误差： 6.62362285381251"
[1] "预测值： 15518.4039237089 实际值： 15231 误差： 1.88696686828751"
[1] "预测值： 15667.3146679094 实际值： 16029 误差： -2.25644352168343"
[1] "预测值： 14004.8493543968 实际值： 14618 误差： -4.19449066632385"
[1] "预测值： 14298.0106360029 实际值： 14775 误差： -3.22835440945558"