A morphological perceptron with gradient-based learning for Brazilian stock market forecasting

Several linear and non-linear techniques have been proposed to solve the stock market forecasting problem. However, a limitation arises from all these techniques and is known as the random walk dilemma (RWD). In this scenario, forecasts generated by arbitrary models have a characteristic one step ahead delay with respect to the time series values, so that, there is a time phase distortion in stock market phenomena reconstruction. In this paper, we propose a suitable model inspired by concepts in mathematical morphology (MM) and lattice theory (LT). This model is generically called the increasing morphological perceptron (IMP). Also, we present a gradient steepest descent method to design the proposed IMP based on ideas from the back-propagation (BP) algorithm and using a systematic approach to overcome the problem of non-differentiability of morphological operations. Into the learning process we have included a procedure to overcome the RWD, which is an automatic correction step that is geared toward eliminating time phase distortions that occur in stock market phenomena. Furthermore, an experimental analysis is conducted with the IMP using four complex non-linear problems of time series forecasting from the Brazilian stock market. Additionally, two natural phenomena time series are used to assess forecasting performance of the proposed IMP with other non financial time series. At the end, the obtained results are discussed and compared to results found using models recently proposed in the literature.

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