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Economic Modeling Using Artificial Intelligence Methods examines the appliance of synthetic intelligence strategies to mannequin financial data. Historically, financial modeling has been modeled within the linear area the place the rules of superposition are legitimate. The appliance of synthetic intelligence for financial modeling permits for a versatile multi-order non-linear modeling. As well as, game concept has largely been utilized in financial modeling. Nevertheless, the inherent limitation of game principle when coping with many player video games encourages using multi-agent techniques for modeling financial phenomena.
The substitute intelligence methods used to mannequin financial data embrace:multi-layer perceptron neural networksradial foundation functionssupport vector machinesrough setsgenetic algorithmparticle swarm optimizationsimulated annealingmulti-agent systemincremental learningfuzzy networks
Sign processing methods are explored to research financial data, and these methods are the time area strategies, time-frequency area strategies and fractals dimension approaches. Fascinating financial issues corresponding to causality versus correlation, simulating the inventory market, modeling and controling inflation, choice pricing, modeling financial progress in addition to portfolio optimization are examined. The connection between financial dependency and interstate battle is explored, and information on how economics is beneficial to foster peace – and vice versa – is investigated. Economic Modeling Using Artificial Intelligence Methods offers with the difficulty of causality within the non-linear area and applies the automated relevance willpower, the proof framework, Bayesian strategy and Granger causality to know causality and correlation.
Economic Modeling Using Artificial Intelligence Methods makes an essential contribution to the world of econometrics, and is a beneficial supply of reference for graduate college students, researchers and monetary practitioners.
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