Título:
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Forecasting financial time series using multiple Kernel Learning
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Autor/a:
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Fábregues de los Santos, Luis
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Otros autores:
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Arratia Quesada, Argimiro Alejandro; Belanche Muñoz, Luis Antonio |
Abstract:
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En col·laboració amb la Universitat de Barcelona (UB) i la Universitat Rovira i Virgili (URV) |
Abstract:
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This thesis introduces a forecasting procedure based on Multiple Kernel Learning to predict and measure
the influence of several economic variables in the process of predicting the equity premium of the S&P 500
Index. In the experiments of Welch and Goyal they determined that, using linear models, those economic
variables had an unreliable effect on the predictive capabilities of the models. The experiments performed
in this thesis with MKL use the same data in an attempt to predict with non-linear models. The kernels
that are part of the MKL procedure are multivariate dynamic kernels adapted for time series. The presented
financial variables have a questionable impact on the predictive capabilities of the developed models due to
the data being noisy. Some of the kernel methods for time series may not be able to extract any relevant
information from exogenous variables, as they are matched in the results by a simple RBF kernel. MKL
shows a poor capacity at selecting the best combination of kernels as it is also matched by RBF, and even
the kernels that MKL uses. However the experimental results show that the presented methods have better
predictive capabilities than the linear models. |
Materia(s):
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-Àrees temàtiques de la UPC::Informàtica -Kernel functions -Computer algorithms -Support vector machines -Màquina de vector de suport -Series temporals financeres -Multiple kernel learning -Predicció de series temporals -Support vector machines -Financial time series -Time series forecasting -Kernel, Funcions de -Algorismes computacionals |
Derechos:
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Tipo de documento:
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Trabajo fin de máster |
Editor:
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Universitat Politècnica de Catalunya
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