Título: A Quantum-Inspired Morphological Approach to Solve the Random Walk Dilemma for Financial Forecasting
Autores: Araújo, Ricardo de A.
Resumo: The financial forecasting is considered a rather difficult problem due to many complex features present in these time series. Several linear and nonlinear techniques have been proposed in the literature to solve this problem. However, a dilemma arises from all these techniques, known as random walk dilemma, where the forecasts generated show a characteristic one step delay regarding the real time series data, that is, a time phase distortion in the reconstruction of phase space of financial phenomena. In this sense, this work presents a quantum-inspired evolutionary learning process with automatic phase adjustment to design the dilation-erosion perceptron (DEP) in order to overcome the random walk dilemma for financial forecasting. Furthermore, an experimental analysis is presented using the Dow Jones Industrial Average Index, where five well-known performance metrics and an evaluation function are used to assess forecasting performance.
Palavras-chave: Dilation-Erosion Perceptron; Quantum-Inspired Evolutionary Learning; Financial Time Series Forecasting
Código DOI: 10.21528/CBIC2011-18.4
Artigo em pdf: st_18.4.pdf
Arquivo BibTex: st_18.4.bib