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- Tiago Alessandro Espínola Ferreira, Germano C. Vasconcelos, Paulo J. L. Adeodato
- Neural Processing Letters
- 2008

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… (More)

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… (More)

- Domingos S. P. Salazar, Paulo J. L. Adeodato, Adrian L. Arnaud
- 2012 IEEE Conference on Evolving and Adaptive…
- 2012

This work describes the first place winner forecasting method for solving the 1st International Competition on Time Series Forecasting (ICTSF 2012). It is based on an already award winning approach of MLP ensembles [1]. The ICTSF 2012 consisted on predicting 8 time series of different time frequency and different forecasting horizons. The main feature of… (More)

This paper introduces a pRAM (probabilistic RAM) node system for sequential pattern veriication. It includes a recurrent network trained with reinforcement learning based on the current state training strategy to generate the target for the reward/penalty signal. The main issues concerning the architecture's applicability to sequential pattern veriication… (More)

- Tiago Alessandro Espínola Ferreira, Germano C. Vasconcelos, Paulo J. L. Adeodato
- 2007 IEEE Symposium on Computational Intelligence…
- 2007

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… (More)

- Marcílio Carlos Pereira de Souto, Paulo J. L. Adeodato, Teresa Bernarda Ludermir
- Int. J. Neural Syst.
- 1999

A fundamental question in the field of artificial neural networks is what set of problems a given class of networks can perform (computability). Such a problem can be made less general, but no less important, by asking what these networks could learn by using a given training procedure (learnability). The basic purpose of this paper is to address the… (More)

- Domingos S. P. Salazar, Paulo J. L. Adeodato, Adrian L. Arnaud
- IEEE Trans. Neural Netw. Learning Syst.
- 2014

This brief generalizes the forecasting method that has been awarded first-place winner in the International Competition of Time Series Forecasting (ICTSF 2012). It is based on a short-term forecasting approach of multilayer perceptrons (MLP) ensembles, combined dynamically with a long-term forecasting. The main feature of this general approach is the… (More)

This paper proposes a new hybrid approach which combines simulated annealing and standard backpropagation for optimizing Multi Layer Perceptron Neural Networks for time series prediction. Experimental results have shown that this approach selects the appropriate time series lags and builds an MLP with adequate number of hidden neurons required for achieving… (More)

- Paulo J. L. Adeodato, Germano C. Vasconcelos, Adrian L. Arnaud, Rodrigo C. L. V. Cunha, Domingos S. M. P. Monteiro, Rosalvo F. Oliveira Neto
- Strategic Advancements in Utilizing Data Mining…
- 2008

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… (More)