An Evolutionary Morphological Approach for Financial Time Series Forecasting

This paper presents an evolutionary morphological approach for designing translation invariant operators for time series forecasting. It consists of an intelligent evolutionary model composed of a modular morphological neural network (MMNN) trained via an improved genetic algorithm (IGA) having optimal genetic operators to accelerate convergence of the genetic algorithm. The proposed design strategy searches for the minimum number of time lags to represent the time series, as well as the weights, architecture and number of modules of the MMNN. An experimental analysis is conducted with the proposed method using six real world financial time series and five well-known performance measurements, demonstrating good performance of MMNN systems for financial time series forecasting.