Unable to predict data well enough
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I have a dataset of 19 non linear regression data points. I am training the first 10 and trying to predict the next 9 values using different Neural Network. However, after trying out mulitple Neural Networks such as Radial Basis Function, Baysian Reguralization BackPropogation, Function Fitting Neural Network and LSTM, i am still not getting good prediction results. I have attached the data where X is the input and Y is the output. First 10 data points are being used for training and next 9 points are used for testing. I have also attached by code which uses Baysian Regularization backpropogation.
Attached is the dataset link :
The results obtained using Baysian Regularization backpropogation are shown in graph attached below :
The code used is as follows:
out_col = 2;
inp_col = 1;
n = 7;
Neurons = 5;
XValidation = data(n+1,inp_col);
net = feedforwardnet(Neurons,'trainbr');
[net,tr] = train(net,X_Train',Y_Train');
y = net(XValidation')';
Kindly let me know how can i improve the prediction results?
More Answers (1)
John D'Errico on 11 Nov 2019
Edited: John D'Errico on 11 Nov 2019
I would point out that your code shows you using the first SEVEN data points, not the first 10, despite your claim otherwise.
n = 7;
As well, since those first two points are completely inconsistent with the rest of your data, I'd expect to see a serious problem in any intelligent long term extrapolation. You gave it 7 data points, and 28% of your data was completely useless crapola. Just because someone else had success does not mean that your data is as good as theirs.
I would instead, suggest that you really try using the first 10 data points. Better yet, try using points 3:10 to train the net. Then see how well prediction actually proceeds for points 11-19.
Remember that extrapolation is always a risky business, prone to failure. If it was always so easy to do, then we would always have perfectly accurate weather forecasts.