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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 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)
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 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)
abstract 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)
Machine learning approaches have been successfully applied for automatic decision support in several domains. The quality of these systems, however, degrades severely in classification problems with small and unbalanced data sets for knowledge acquisition. Inherent to several real-world problems, data sets with these characteristics are the reality to be(More)
The objective of this paper is providing an integrated environment for knowledge reuse in KDD, for preventing recurrence of known errors and reinforcing project successes, based on previous experience. It combines methodologies from project management, data warehousing, mining and knowledge representation. Different from purely algorithmic papers, this one(More)