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Financial time series_forecasting_svm

Financial Time series Forecasting using support vector machines ( Elaborated by Mohamed DHAOUI , 3rd engineering student at Tunisia Polytechnic School ) .

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Financial time series_forecasting_svm

  1. 1. Tunisia Polytechnic School Data Mining project Presented by Mohamed DHAOUI 3rd year engineering student (contact@Mohamed-dhaoui.com) Academic Year : 2015-2016 Financial time series forecasting using support vector machines
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  14. 14. a weight parameter, which needs to be carefully set 14
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  28. 28.  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, 28
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  30. 30.  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 30
  31. 31.  Weights updating  Δwt = -e* E + α Δwt-1  =H* δ0 e: learning rate α :momentun Wh,o Hidden layer Output layer O H E= actual-ideal δ0= -E*f’(o) δk= f’(h)*Wh,o *δ0 31
  32. 32.  Weaknesses • Gradient descent with backpropagation is not guaranteed to find the global minimum. • There is no rule for selecting the best learning rate and the momentum. • Slow algorithm that need a computational resources. 32
  33. 33. SVM perfermance • Too small value for C caused underfit the training data while too large a value of C caused overfit the training data 33
  34. 34. the best prediction performance of the holdout data is recorded when delta is 25 and C is 78 SVM perfermance 34
  35. 35. 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%. 35
  36. 36. 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 36

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