RiA Prediction Systems

Recife, Brazil

RiA Prediction Systems

Recife, Brazil

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Araujo R.D.A.,RiA Prediction Systems | Oliveira A.L.I.,Federal University of Pernambuco | Soares S.,Federal University of Pernambuco
Expert Systems with Applications | Year: 2011

Abstract: This work presents a shift-invariant morphological system to solve the problem of software development cost estimation (SDCE). It consists of a hybrid morphological model, which is a linear combination between a morphological-rank (MR) operator (nonlinear) and a Finite Impulse Response (FIR) operator (linear), referred to as morphological-rank-linear (MRL) filter. A gradient steepest descent method to adjust the MRL filter parameters (learning process), using the Least Mean Squares (LMS) algorithm, and a systematic approach to overcome the problem of non-differentiability of the morphological-rank operator are used to improve the numerical robustness of the training algorithm. Furthermore, an experimental analysis is conducted with the proposed system using the NASA software project database, and in the experiments, two relevant performance metrics and an evaluation function are used to assess its performance. The results obtained are compared to models recently presented in literature, showing superior performance of this kind of morphological systems for the SDCE problem. © 2010 Elsevier Ltd. All rights reserved.


De Araujo R.A.,RiA Prediction Systems | Oliveira A.L.I.,Federal University of Pernambuco | Soares S.,Federal University of Pernambuco
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics | Year: 2010

This paper proposes the Covariance Matrix Adaptation based Evolutionary (CMAbE) methodology to overcome the random walk dilemma, characterized by one step delay regarding the real time series values, adjusting time phase distortions in the financial time series forecasting problem. The proposed CMAbE methodology consists of a hybrid model composed of the MultiLayer Perceptron (MLP) networks and the Covariance Matrix Adaptation Evolution Strategy (CMAES), which searches for the best particular time lags to optimally describe the time series phenomenon, as well for the best architecture, parameters and training algorithm of MLP networks. 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 recently methods presented in literature. ©2010 IEEE.


Araujo R.D.A.,RiA Prediction Systems
Information Sciences | Year: 2010

In this paper, we present a method to overcome the random walk (RW) dilemma for financial time series forecasting, called swarm-based translation-invariant morphological prediction (STMP) method. It consists of a hybrid model composed of a modular morphological neural network (MMNN) combined with a particle swarm optimizer (PSO), which searches for the best time lags to optimally describe the time series phenomenon, as well as estimates the initial (sub-optimal) parameters of the MMNN (weights, architecture and number of modules). An additional optimization is performed with each particle of the PSO population (a distinct MMNN) using the back-propagation (BP) algorithm. After the MMNN parameters adjustment, we use a behavioral statistical test and a phase fix procedure to adjust time phase distortions observed in financial time series. Finally, we conduct an experimental analysis with the proposed method using four real world stock market time series, where five well-known performance metrics and a fitness function are used to assess the prediction performance. The obtained results are compared with those generated by classical models presented in the literature. © 2010 Elsevier Inc. All rights reserved.


De A. Araujo R.,RiA Prediction Systems
Knowledge-Based Systems | Year: 2011

In this work a class of hybrid morphological perceptrons, called dilation-erosion perceptron (DEP), is presented to overcome the random walk dilemma in the time series forecasting problem. It consists of a convex combination of fundamental operators from mathematical morphology (MM) on complete lattices theory (CLT). A gradient steepest descent method is presented to design the proposed DEP (learning process), using the back propagation (BP) algorithm and a systematic approach to overcome the problem of nondifferentiability of morphological operators. The learning process includes an automatic phase fix procedure that is geared at eliminating time phase distortions observed in some time series. Finally, an experimental analysis is conducted with the proposed DEP using five real world time series, where five well-known performance metrics and an evaluation function are used to assess the forecasting performance of the proposed model. The obtained results are compared with those generated by classical forecasting models presented in the literature. © 2011 Elsevier B.V. All rights reserved.


Araujo R.D.A.,RiA Prediction Systems
Expert Systems with Applications | Year: 2011

This work presents a method, named Translation Invariant Morphological Time-lag Added Evolutionary Forecasting (TIMTAEF), to overcome the random walk (RW) dilemma for stock market prediction, performing an evolutionary search for the minimum dimension in determining the characteristic phase space that generates the financial time series phenomenon. It is inspired on Takens Theorem and consists of an intelligent hybrid model composed of a Modular Morphological Neural Network (MMNN) combined with a Modified Genetic Algorithm (MGA), which searches for the particular time lags capable of a fine tuned characterization of the time series and estimates the initial (sub-optimal) parameters (weights, architecture and number of modules) of the MMNN. Each individual of the MGA population is trained by the Back Propagation (BP) algorithm to further improve the MMNN parameters supplied by the MGA. After adjusting the model, it performs a behavioral statistical test and a phase fix procedure to adjust time phase distortions observed in financial time series. Furthermore, an experimental analysis is conducted with the proposed model using four real world stock market time series. Five well-known performance metrics and an evaluation function are used to assess the performance of the proposed model and the obtained results are compared to classical models presented in literature. © 2010 Elsevier Ltd. All rights reserved.

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