ABSTRACT The need for computational resources grows as computational algorithms gain popularity in different sectors of the scientific community. Sequential codes need to be converted to parallel versions to optimize the use of these resources. Maintaining a local infrastructure for the execution of distributed computing, through desktop grids, for example, has been replaced in favor of cloud platforms that abstract the complexity of these local infrastructures. Unfortunately, the cost of accessing these resources could leave out various studies that could be carried by a simpler infrastructure. In this article, we present a platform for distributing computer simulations on resources available on a local network using container virtualization that abstracts the complexity needed to configure these execution environments and allows any user can benefit from this infrastructure. Simulations could be developed in any programming language (such as Python, Java, C, and R) and with specific execution needs within reach of the scientific community in a general way. We will present results obtained in running simulations that required more than 1000 runs with different initial parameters and various other experiments that benefited from using the platform.

This article proposes a computational procedure that applies a quantum algorithm to train classical artificial neural networks. The goal of the procedure is to apply quantum walk as a search algorithm in a complete graph to find all synaptic weights of a classical artificial neural network. Each vertex of this complete graph represents a possible synaptic weight set in the$w$-dimensional search space, where$w$is the number of weights of the neural network. To know the number of iterations requireda priorito obtain the solutions is one of the main advantages of the procedure. Another advantage is that the proposed method does not stagnate in local minimums. Thus, it is possible to use the quantum walk search procedure as an alternative to the backpropagation algorithm. The proposed method was employed for a$XOR$problem to prove the proposed concept. To solve this problem, the proposed method trained a classical artificial neural network with nine weights. However, the procedure can find solutions for any number of dimensions. The results achieved demonstrate the viability of the proposal, contributing to machine learning and quantum computing researches.

The simplest linear combination of time series forecasters has shown a better performance than individual models. Thus, many researchers have sought to combine models for improving the forecasting process. This paper introduces a copulas-based approach (CB) for aggregating forecasters. The CB is based on the Cacoullos copula, with architecture divided into three parts: Single Modelling, Marginal Probability Distribution Computational and Joint Probability Distribution Computation. In the first part, the forecasts of individual models are obtained. In the second part, the residuals of the individuals models are calculated. In the third part, the models are combined via Cacoullos copula, based on the obtained residuals. The paper also evaluates the performance of the CB via simulated as well as financial time series (e.g. Google Stock Value). Thus, a comparative analysis is presented between CB and individual models (e.g. Artificial Neural Networks) and alternative combined forecasters (e.g. Simple Average-SA and Normal copula-CN). This study showed that the CB model produces better results when compared with the individual models, SA and CN.

The stock market is the most important institution for global investments all around the world. Among the possibles analysis, the study and forecasting of ultra-high-frequency time series is an interesting and great challenge to econometric modeling and statistical analysis due its complex behaviour. This work proposes a hybrid intelligent system to forecast ultra-high-frequency stock prices. The intelligent system is composed of a genetic algorithm (GA) that seeks the best parameters (the number of nodes in the input and hidden layers, and the training algorithm) of an artificial neural network (ANN) of type multiLayer perceptron (MLP). The proposed method called Time-delay Added Evolutionary Forecasting (TAEF) is a data-driven approach, that performs a pos-processing, where the objective is to reduce the difference between the forecasting and the actual series. The experimental study is performed using ultra-high frequency time series (Amazon, APPLE, Google and Intel stock prices) and shows that the proposed approach overcomes classical techniques of the computational intelligence and statistics in light of six relevant evaluation measures.

Time series combined forecasters have been superior to the respective single models in statistical terms. In this way, the linear combination functions, e.g. the simple average (SA) and the minimal variance (MV) approaches, have been the main alternatives for aggregation in the literature. In this work, it is proposed a copulas-based method for combining biased single models. Copulas are multivariate functions that operate on marginal probability distributions, allowing one to model the forecasters errors and then the dependence among them: a typical divide-and-conquer framework that can result in nonlinear accurate combined forecasters. The performance of the copulas-based combination method is assessed by means of a comparison with SA and MV models, based on two financial time series.

In this paper, a simulation of intelligent agents is developed to recreate the environment of negotiation of stock markets. The focus is analyze the behavior of movement/ fluctuation of stock markets. This movement can be captured by a measure called volatility, which is the difference between two stock prices in distinct periods. It characterizes the sensibility of a market change in the world economy. The contributions of this work are three-fold: (i) a simulation of dynamics of stock markets based in intelligent agents; (ii) based in this simulation an analysis of the volatility dynamic of the simulated time series; (iii) after that, a investigation about the relationship between the volatility of the markets, distribution of gain/loss money of agents and the coefficient of the exponential function based on the ideal gas theory of Maxwell-Boltzmann. This information can be used, for example, to predict the future behavior of the markets.

In this paper it is introduced a new perturbative approach for time series forecasting. The model uses the error of the series, that is the difference between real value of the series and the output of a predictive method, to improve the series forecasting. The methodology proposed is inspired in the Perturbation Theory, that consists in a set of approximation schemes used to describe a complicated problem in terms of simpler ones. For an experimental investigation, this theory, is combined with the TAEF method, that has interesting results when compared with the literature. This combination is called P-TAEF (Perturbative TAEF). Its results over some time series are discussed and compared with previous results found in the literature. It was used several performance measures that showed the robustness of the perturbative approach.

The Perturbative Time-delay Added Evolutionary Forecasting (P-TAEF) method for time series prediction is inspired in the Perturbation Theory, a concept already commonly used in other areas of science (physics, mathematics, etc), and evolutionary computing. This methodology is shown and an experimental investigation is conducted with some relevant time series and the results achieved are discussed and compared with previous results found in the literature.

Forecasting systems have been widely used for decision making and one of its most promising approaches is based on Artificial Neural Networks (ANN). In this paper, a hybrid swarm system is presented for the time series forecasting problem, which consists of an intelligent hybrid model composed of an ANN combined with Particle Swarm Optimizer (PSO). The proposed method searches the relevant time lags for a correct characterization of the time series, as well as the number of processing units in the hidden layer, the training algorithm and the modeling of ANN. The proposed method shows an efficient procedure to adjust the ANN parameters through the use of a particle swarm optimization mechanism. An experimental analysis is conducted with the proposed method using six real world time series and the results are discussed according to five performance measures.

Artificial Neural Networks (ANN) have been widely used in order to solve the time series forecasting problem. One of its most promising approaches is the combination with other intelligence techniques, as genetic algorithms, evolutionary strategies, etc. The efficiency of these technics, if used correctly, can be very high. Unfortunately, in terms of fitness function, there is still some lacks of experimental (and theoretical) results to help the practitioners to use these technics in order to find better predictions. This paper proposes others fitness functions (instead of conventional MSE based) and presents an experimental investigation of eight different fitness functions for time series prediction based on five well known measures of statistical performance in the literature. Using a hybrid method for tuning of the ANN structure and parameters (a modified genetic Algorithm), an analysis of the final results effects are made according with four relevant time series. This work shows that small changes of the fitness function evaluation can lead to a significantly improved performance.

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.

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.

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.

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.

In this paper, a hybrid evolutionary Morphological-Rank-Linear (MRL) approach is proposed for time series forecasting. The proposed method consists of an Intelligent Hybrid Evolutionary MRL (IHEMRL) model composed of an MRL filter and a modified Genetic Algorithm (GA) that employs optimal genetic operators that accelerate its search convergence. The modified GA 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 (mixing parameter (lambda), rank (r), linear Finite Impulse Response (FIR) filter (6) and the Morphological-Rank (MR) filter (a) coefficients). Thus, each individual of the GA population is trained by the averaged Least Mean Squares (LMS) algorithm to further improve the MRL filter parameters supplied by the GA. Experiments are conducted with the proposed approach using three real world time series according to a group of relevant performance metrics and the results are compared both to ARIMA models and MultiLayer Perceptrons (MLP).

This paper proposes a hybrid differential evolutionary system (HDES) for financial time series forecasting, which performs a differential evolutionary search for the minimum dimension to determining the characteristic phase space that generates the time series phenomenon. It consists of an intelligent hybrid model composed of an artificial neural network (ANN) combined with the improved differential evolution (IDE). The proposed IDE searches for the relevant time lags for a correct time series characterization, the number of processing units in the ANN hidden layer, the ANN training algorithm and the modeling of ANN. Initially, the proposed HDES chooses the most tuned prediction model for time series representation, thus it performs a behavioral statistical test in the attempt to adjust forecast time phase distortions that appear in financial time series. An experimental analysis is conducted with the proposed HDES using two real world financial time series and five well-known performance metrics are used to assess its performance. The obtained results are compared to time-delay added evolutionary forecasting (TAEF) method.

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

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.

This paper presents an improved evolutionary hybrid method for designing morphological operators via the Matheron and the Banon and Barrera decompositions of translation invariant operators. It consists of a hybrid model composed of a modular morphological neural network (MMNN) and an improved genetic algorithm (IGA) having optimal genetic operators to accelerate convergence of the genetic algorithm. The proposed design method looks for initial weights, architecture and number of modules in the MMNN; then each element of the IGA population is trained via the back propagation (BP) algorithm. Optimal morphological operators are applied to image restoration and edge extraction of binary images corrupted by salt and pepper noise. The method proposed herein is capable of performing simultaneous edge extraction and noise removal operations, allowing seamless and efficient design of morphological operators of either increasing or non-increasing types.

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.

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.