Tiago A. E. Ferreira

An intelligent hybrid morphological-rank-linear method for financial time series prediction

In this paper the morphological-rank-linear time-lag added evolutionary forecasting (MRLTAEF) method is proposed in order to overcome the random walk dilemma for financial time series prediction. It consists of an intelligent hybrid model composed of a morphological-rank-linear (MRL) filter 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 for the estimation of the initial (sub-optimal) parameters of the MRL filter. Each individual of the MGA population is trained by the averaged least mean squares (LMS) algorithm to further improve the parameters of the MRL filter supplied by the MGA. Initially, the proposed MRLTAEF method chooses the most tuned predictive model for time series representation, and then performs a behavioral statistical test in the attempt to adjust time phase distortions that appear in financial time series. Experiments are conducted with the proposed MRLTAEF method using six real-world financial time series according to a group of relevant performance metrics and the results are compared to multilayer perceptron (MLP) networks, MRL filters and the previously introduced time-delay added evolutionary forecasting (TAEF) method.

A Morphological-Rank-Linear evolutionary method for stock market prediction

This work presents an evolutionary morphological-rank-linear approach in order to overcome the random walk dilemma for financial time series forecasting. The proposed Evolutionary Morphological-Rank-Linear Forecasting (EMRLF) method consists of an intelligent hybrid model composed of a Morphological-Rank-Linear (MRL) filter combined with a Modified Genetic Algorithm (MGA), which performs an evolutionary search for the minimum number of relevant time lags capable of a fine tuned characterization of the time series, as well as for the initial (sub-optimal) parameters of the MRL filter. Then, each individual of the MGA population is improved using the Least Mean Squares (LMS) algorithm to further adjust the parameters of the MRL filter, supplied by the MGA. After built the prediction model, the proposed method performs a behavioral statistical test with a phase fix procedure to adjust time phase distortions that can appear in the modeling of financial time series. An experimental analysis is conducted with the method using four real world stock market time series according to a group of performance metrics and the results are compared to both MultiLayer Perceptron (MLP) networks and a more advanced, previously introduced, Time-delay Added Evolutionary Forecasting (TAEF) method.

A new evolutionary method for time series forecasting

This paper presents a new method --- the Time-delay Added Evolutionary Forecasting (TAEF) method --- for time series prediction which performs an evolutionary search of the minimum necessary number of dimensions embedded in the problem for determining the characteristic phase space of the time series. The method proposed is inspired in F. Takens theorem and consists of an intelligent hybrid model composed of an artificial neural network (ANN) combined with a modified genetic algorithm (GA). Initially, the TAEF method finds the most fitted predictor model for representing the series and then performs a behavioral statistical test in order to adjust time phase distortions.

A New Evolutionary Approach for Time Series Forecasting

This work introduces a new method for time series prediction - time-delay added evolutionary forecasting (TAEF) - that carries out an evolutionary search of the minimum necessary time lags embedded in the problem for determining the phase space that generates the time series. The method proposed consists of a hybrid model composed of an artificial neural network (ANN) combined with a modified genetic algorithm (GA) that is capable to evolve the complete network architecture and parameters, its training algorithm and the necessary time lags to represent the series. Initially, the TAEF method finds the most fitted predictor model and then performs a behavioral statistical test in order to adjust time phase distortions that may appear in the representation of sonic series. An experimental investigation is conducted with the method with sonic relevant time series and the results achieved are discussed and coin pared, according to several performance measures, to results found with the multilayer perteptron networks and other works reported in the literature

Morphological-Rank-Linear Time-lag Added Evolutionary Forecasting method for financial time series forecasting

This paper proposes the Morphological-Rank-Linear Time-lag Added Evolutionary Forecasting (MRLTAEF) method for financial time series forecasting, which performs an evolutionary search for the minimum number of relevant time lags necessary to efficiently represent complex time series. It consists of an intelligent hybrid model composed of a Morphological-Rank-Linear (MRL) filter combined with a Modified Genetic Algorithm (MGA) which employs optimal genetic operators in order to accelerate its search convergence. The MGA searches for the particular time lags capable of a fine tuned characterization of the time series and estimates the initial (sub-optimal) parameters of the MRL filter - the mixing parameter (lambda), the rank (r), the coefficients of the linear Finite Impulse Response (FIR) filter (b) and the coefficients of the Morphological-Rank (MR) filter (a). Thus, each individual of the MGA population is trained by the averaged Least Mean Squares (LMS) algorithm to further improve the parameters of the MRL filter supplied by the MGA. Initially, the proposed MRLTAEF method chooses the most tuned prediction model for time series representation, thus it performs a behavioral statistical test in the attempt to adjust forecasting time phase distortions that appear in financial time series. Experiments are conducted with the proposed MRLTAEF method using three real world financial time series according to a group of relevant performance metrics and the results are compared to MultiLayer Perceptron (MLP) networks, MRL filters and the previously introduced Time-delay Added Evolutionary Forecasting (TAEF) method.

A hybrid intelligent system approach for improving the prediction of real world time series

This work presents a new procedure for the solution of time series forecasting problems which searches for the necessary minimum quantity of dimensions embedded in the problem for determining the characteristic phase space of the phenomenon generating the time series. The proposed system is inspired in F. Takens theorem (1980) and consists of an intelligent hybrid model composed of an artificial neural network (ANN) combined with a modified genetic algorithm (GA). It is shown how this proposed model can boost the performance of time series prediction of both artificially generated time series and real world time series from the financial market. An experimental investigation is conducted with the introduced method with five different relevant time series and the results achieved are discussed and compared with previous results found in the literature, showing the robustness of the proposed approach.

A Quantum-Inspired Intelligent Hybrid method for stock market forecasting

This work introduces a quantum-inspired intelligent hybrid (QIIH) method for stock market forecasting. It performs a quantum-inspired evolutionary search for the minimum necessary dimension (time lags) embedded in the problem for determining the characteristic phase space that generates the financial time series phenomenon. The proposed QIIH method consists of a quantum-inspired intelligent hybrid model composed of an artificial neural network (ANN) with a modified quantum-inspired evolutionary algorithm (MQIEA), which is able to evolve the complete network architecture and parameters (pruning process), its training algorithm (used to further improve the ANN parameters supplied by the MQIEA) and the particular time lags capable of a fine tuned time series characterization. Initially, the proposed QIIH method chooses the most fitted forecasting model, thus it performs a behavioral statistical test in the attempt to adjust forecasting time phase distortions that appear in financial time series. Furthermore, an experimental analysis is conducted with the proposed QIIH method using three real world stock market time series, and the achieved results are discussed and compared, according to a group of relevant performance metrics, to results found with MultiLayer Perceptron (MLP) networks and the previously introduced time-delay added evolutionary forecasting (TAEF) method.

An experimental study with a Hybrid method for tuning neural network for time series prediction

This paper presents an study of a new hybrid method based on the greedy randomized adaptive search procedure(GRASP) and evolutionary strategies(ES) concepts for tuning the structure and parameters of an artificial neural network (ANN). It consists of an ANN trained and adjusted by this new method, which searches for the minimum number of (and their specific) relevant time lags for a correct time series representation, the parameters configuration and the weights of the ANN until the learning performance in terms of fitness value is good enough, which found, for an optimal or sub-optimal forecasting model. An experimental analysis is presented with the proposed method using three relevant time series, and its results are discussed according to five well-known performance measures, showing the effectiveness and robustness of the proposed method.

A hybrid method for tuning neural network for time series forecasting

This paper presents an study about a new Hybrid method -GRASPES - for time series prediction, inspired in F. Takens theorem and based on a multi-start metaheuristic for combinatorial problems - Greedy Randomized Adaptive Search Procedure(GRASP) - and Evolutionary Strategies (ES) concepts. The GRAPES tuning and evolve the Artificial Neural Network parameters configuration, the weights and the minimum number of (and their specific) relevant time lags, searching an optimal or sub-optimal forecasting model for a correct time series representation. An experimental investigation is conducted with the GRASPES with some time series and the results achieved are discussed and compared, according to five well-known performance measures, to other works reported in the literature.

A New Hybrid Method for Time Series Forecasting

This paper presents a new method — the Timedelay Added Evolutionary Forecasting (TAEF) method — for time series prediction which performs an evolutionary search of the minimum necessary number of dimensions embedded in the problem for determining the characteristic phase space of the phenomenon generating the time series. The method proposed is inspired in F. Takens theorem and consists of an intelligent hybrid model composed of an artificial neural network (ANN) combined with a modified genetic algorithm (GA). Initially, the TAEF method finds the most fitted predictor model for representing the series and then performs a behavioral statistical test in order to adjust time phase distortions that may appear in the representation of some series. It is shown how this model proposed can boost the performance of time series prediction of both artificially generated time series and real world time series from the financial market. An experimental investigation is conducted with the TAEF method with five different relevant time series and the results achieved are discussed and compared with previous results found in the literature, according to several performance measures, showing the robustness of the proposed approach.