A New Increasing Translation Invariant Morphological Method for Financial Time Series Forecasting

Ricardo de A. AraújoGlaucio G. de M. Melo

This paper presents a new method, referred to as Increasing Translation Invariant Morphological (ITIM), to overcome the random walk dilemma for financial time series forecasting. It consists of a hybrid intelligent model composed of a Modular Morphological Neural Network (MMNN) and a Modified Genetic Algorithm (MGA), which searches for the minimum number of time lags for a fine tuned time series representation, as well as by the initial weights, architecture and number of modules of the MMNN. Each element of the MGA population is trained via Back Propagation (BP) algorithm to further improve the parameters supplied by the MGA. The proposed method, after forecasting model adjustment, performs 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 method using two real world time series and five well-known performance measurements, demonstrating consistent better performance of this kind of morphological system.

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