A quantum-inspired hybrid methodology for financial time series prediction

In this work a quantum-inspired hybrid methodology is proposed to overcome the random walk dilemma for financial time series prediction. It consists of a hybrid model composed of a Qubit Multilayer Perceptron (QuMLP) with a Quantum-Inspired Evolutionary Algorithm (QIEA), which searches for the best particular time lags able to characterize the time series phenomenon, as well as to evolve the complete QuMLP architecture and parameters. Each individual of the QIEA population is adjusted by the Complex Back-Propagation (CBP) algorithm to further improve the QuMLP parameters supplied by the QIEA. After the prediction model search procedure, it uses a behavioral statistical test and a phase fix procedure to adjust time phase distortions that appear in financial time series. An experimental analysis is conducted with the proposed methodology through four real world financial time series, and the obtained results are discussed and compared to results found with Multilayer Perceptiron (MPL) networks and the previously introduced Morphological-Rank-Linear Time-lag Added Evolutionary Forecasting (MRLTAEF) method.