multiple feature for prediction time series data with libsvm
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I am using libSVM in Matlab for prediction time series data. Basically, i use 1 feature (15 min past) to predict the label (the next 15 min value) and i got a good result from there.
data = x(1:end-1); dataLabels = x(2:end);
But, when i used multiple feature (ex: 4 --> 15 min, 30 min,45 min,60 min past data) to predict next value, it gimme an error.
v1 = x(1:end-4);
v2 = x(2:end-3);
v3 = x(3:end-2);
v4 = x(4:end-1);
label = x(5:end);
data = [v1,v2,v3,v4];
dataLabels = label;
%splitting train test data
trainDataLength = round(length(data)*latih/100);
trainSet = data(1:trainDataLength);
trainLabels = dataLabels(1:trainDataLength);
testSet = data(trainDataLength+1:end);
testLabels = dataLabels(trainDataLength+1:end);
[MAPE,acc] = SVR(trainSet,trainLabels,testSet,testLabels,'nov-14');
Error says "Length of label vector does not match # of instances. model file should be a struct array". Then i am transpose the label.
SVR ran with 1 st iter but give no result with following message "label (1st argument) should be a vector (# of column is 1)." "Attempted to access predicted_label(:,1); index out of bounds because size(predicted_label)=[0,0]."
So, what should i do to solving this error ? Thanks in advance :)
回答（2 个）
Stalin Samuel
2015-6-29
0 个投票
lemon Nation
2019-9-23
0 个投票
I am in the same difficulty as you，I think there may be something wrong with the setting of the tag
1 个评论
lemon Nation
2019-9-23
Just now this problem has been solved by me, sharing methods in the following hope can help you
Wrong codes:
clear all,clc
format compact;
nn = 5, n=10
x = -2.5:1.5
err=x.^2
model = svmtrain(err,x,'-s 3 -t 2 -c 2.2 -g 2.8 -p 0.01')
[py,accuracy,dv] = svmpredict(err,x,model)
%%
testx = nn+1:n
testy = zeros(10,1);% 理论y值无所谓
[ptesty,~,~] = svmpredict(testy,testx,model)
err = [py,ptesty]'
%% 出现如下问题：
>>label (1st argument) should be a vector (# of column is 1).
corrected codes
clear all,clc
format compact;
nn = 5, n=10
x = [-2.5:1.5]'
err=x.^2
model = svmtrain(err,x,'-s 3 -t 2 -c 2.2 -g 2.8 -p 0.01')
[py,accuracy,dv] = svmpredict(err,x,model)
%%
testx = nn+1:n
testy = zeros(10,1);% 理论y值无所谓
[ptesty,~,~] = svmpredict(testy,testx,model)
err = [py,ptesty]'
%% Tips
This is the first time answer others' questions and hope I hope I can keep the good habit of keeping records
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2015-6-29
2019-9-23
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