Financial time series_forecasting_svm
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Tunisia Polytechnic School Data Mining project Presented by Mohamed DHAOUI 3 rd year engineering student (contact@Mohamed - dhaoui.com) Academic Year : 2015 - 2016 Financial time series forecasting using support vector machines
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Transcript of Financial time series_forecasting_svm
Tunisia Polytechnic School
Data Mining project
Presented by
Mohamed DHAOUI
3rd year engineering student
([email protected]) Academic Year : 2015-2016
Financial time series forecasting using support vector machines
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a weight parameter, which needs to be carefully set
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Backpropagation, an abbreviation for "backward propagation of errors", is a common method of training artificial neural networks used in conjunction with an optimasition method such as gradient descent. The method calculates the gradient of a loss function with respect to all the weights in the network and try to update these weights,
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Algorithm:
initialize network weight (randomly)
Do
forEach training example ex
prediction = neural-net-output(network, ex)
actual = teacher-output(ex)
compute error (prediction - actual) at the output units
compute for all weights
update network weights
until all examples classified correctly or another stopping criterion satisfied
return the network
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Weights updating
Δwt = -e* E + α Δwt-1
=H* δ0
e: learning rate
α :momentun
Wh,o
Hidden layer
Output layer
O
HE= actual-ideal
δ0= -E*f’(o)
δk= f’(h)*Wh,o *δ0
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Weaknesses
• Gradient descent with backpropagation is notguaranteed to find the global minimum.
• There is no rule for selecting the bestlearning rate and the momentum.
• Slow algorithm that need a computationalresources.
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SVM perfermance
• Too small value for C caused underfit the training data while too large a value of C caused overfit the training data
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the best prediction performance of the holdout data is recorded when delta is 25 and C is 78
SVM perfermance
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BP perfermance
• The best prediction performance for the holdout data is produced when the number of hidden processing elements are 24 and the stopping criteria is 146 400 epochs.
• The prediction performance of the holdout data is 54.7332% and that of the training data is 58.5217%.
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Comparison
SVM outperforms BPN and CBR by 3.0981% and 5.852% for the holdout data, respectively
For the training data, SVM has higher prediction accuracy than BPN by 6.2309%
SVM performs better than CBR at 5% statistical significance level
SVM does not significantly outperform BP BP and CBR do not significantly outperform each
other
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