Financial prediction using modified probabilistic learning network with embedded local linear models

In this paper, a model is proposed which combines multiple local linear models with a novel modified probabilistic neural network (MPNN). The proposed model is shown to provide improved regularization with reduced computation utilizing semiparametric model approach and efficient vector quantization of data space. In this paper, the proposed model is shown to generalize better with reduced variance and model complexity in short-term financial prediction application.

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