Germano C. Vasconcelos

A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks

The Time-delay Added Evolutionary Forecasting (TAEF) approach is a new method for time series prediction that performs an evolutionary search for the minimum number of dimensions necessary to represent the underlying information that generates the time series. The methodology proposed is inspired in Takens theorem and consists of an intelligent hybrid model composed of an artificial neural network combined with a modified genetic algorithm. Initially, the TAEF method finds the best fitted model to forecast 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. An experimental investigation conducted with relevant time series show the robustness of the method through a comparison, according to several performance measures, to previous results found in the literature and those obtained with more traditional methods.

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.

Hybrid differential evolutionary system for financial time series forecasting

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.

A Neural Network Based Solution for the Credit Risk Assessment Problem

The automation of decision making in financial markets is one of the major application areas of neural networks. Risk analysis is one of the problems where the technique has been efficiently applied. This paper investigates a solution to a credit analysis problem in a rather peculiar environment, characterized by a stabilized economy but subject to a high interest rate, namely the Brazilian market. A neural network based credit scoring system has been developed for the retail business in Brazil and its performance has been evaluated against that attained by a traditional discriminant analysis system. Extensive experimental results carried out with a database of 18,000 consumers of a leading Brazilian supermarket chain clearly indicate that a better solution is found with the connectionist based system.

The Power of Sampling and Stacking for the PAKDD-2007 Cross-Selling Problem

This article presents an efficient solution for the PAKDD-2007 Competition cross-selling problem. The solution is based on a thorough approach which involves the creation of new input variables, efficient data preparation and transformation, adequate data sampling strategy and a combination of two of the most robust modeling techniques. Due to the complexity imposed by the very small amount of examples in the target class, the approach for model robustness was to produce the median score of the 11 models developed with an adapted version of the 11-fold cross-validation process and the use of a combination of two robust techniques via stacking, the MLP neural network and the n-tuple classifier. Despite the problem complexity, the performance on the prediction data set (unlabeled samples), measured through KS2 and ROC curves was shown to be very effective and finished as the first runner-up solution of the competition.

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

A Robust Method for the VRPTW with Multi-Start Simulated Annealing and Statistical Analysis

Vehicle routing problems have been extensively analyzed to reduce transportation costs. More particularly, the vehicle routing problem with time windows (VRPTW) imposes the period of time of customer availability as a constraint, a very common characteristic in real world situations. Using minimization of the total distance as the main objective to be fulfilled, this work implements an efficient hybrid system which associates non-monotonic simulated annealing to hill climbing with random restart (multi-start). Firstly, the algorithm is compared to the best results published in the literature for the 56 Solomon instances. Then, it is shown how statistical methods - analysis of variance and linear regression - can be used to determine the significance degree of the systems parameters to obtain an even better and more reliable performance

A Multi-Start Simulated Annealing Algorithm for the Vehicle Routing Problem with Time Windows

Vehicle Routing Problems have been analyzed to reduce transportation costs of people and goods. More particularly, the Vehicle Routing Problem with Time Windows (VRPTW) imposes the period of time of customer availability as a constraint, a very common characteristic in real world picking up and delivery problems. Using minimization of the total distance as the main objective to be fulfilled, this work implements an efficient hybrid system which associates a nonmonotonic Simulated Annealing technique to a Hill Climbing Strategy with Random Restart (Multi-Start). The algorithm performance is evaluated by comparing the results achieved with the best published works found in the literature of the 56 Solomon instances. The results outperformed or paired the individual best previous results in 36 out of the 56 instances.

Neural Networks vs Logistic Regression: a Comparative Study on a Large Data Set

Neural networks and logistic regression have been among the most widely used AI technique in applications of pattern classification.Much has been discussed about if there is any significant difference in between them but much less has been actually done with real-world applications data (large scale) to help settle this matter, with a few exceptions.This paper presents a performance comparison between these two techniques on the market application of credit risk assessment, making use of a large database from an outstanding credit bureau and financial institution (a sample of 180,000 examples).The comparison was carried out through a 30-fold stratified cross-validation process to define the confidence intervals for the performance evaluation. Several metrics were applied both on the optimal decision point and along the continuous output domain.The statistical tests showed that multilayer perceptrons perform better than logistic regression at 95% confidence level, for all the metrics used.

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.