Bayesian forecaster using class-based optimization

Abstract

Suppose that several forecasters exist for the problem in which class-wise accuracies of forecasting classifiers are important. For such a case, we propose to use a new Bayesian approach for deriving one unique forecaster out of the existing forecasters. Our Bayesian approach links the existing forecasting classifiers via class-based optimization by the aid of an evolutionary algorithm (EA). To show the usefulness of our Bayesian approach in practical situations, we have considered the case of the Korean stock market, where numerous lag-l forecasting classifiers exist for monitoring its status.

Statistical user model supported by R-Tree structure

“…There also can be found solutions suited to high-dimensionality data [36]. On the other hand, there also can be found alternatives substituting either the fusion procedure (such as computing centroids), or the matching function [2] during inferences and/or insertions. Besides, self-adaptive formulation is seen as an appealing challenge for further work.…”

Using a case-based reasoning approach for trading in sports betting markets

“…Techniques from Artificial Intelligence such as rule-based systems or artificial neural networks have been broadly used for learning and evolving these artificial financial traders. Other works represent stock, commodity or foreign exchange markets as time series and study the evolution of the assets over time, trying to predict future prices [24,16,32,18,3,22]. The price evolution in financial markets is dependent on external information whereas the odds evolution of sports betting markets is dependent on the outcomes probabilities.…”