Francisco Javier de Cos Juez

TL;DR: This research proposes a hybrid system which combines fuzzy clustering and MARS, and shows that the hybrid model outperforms the other systems, both in terms of the percentage of correct classifications and in Terms of the profit generated by the lending decisions.

TL;DR: The algorithm combines time series analysis methods to forecast the values of the predictor variables with machine learning techniques to predict RUL from those variables, and shows a high prediction capability, far greater than that provided by the VARMA model.

TL;DR: This paper examines the forecasting performance of ARIMA and two different kinds of artificial neural networks models (multilayer perceptron and Elman) using published data of copper spot prices from the New York Commodity Exchange, (COMEX).

TL;DR: In this article, a multivariate adaptive regression splines (MARS) technique was used to estimate the state of charge (SOC) of a high capacity LiFePO4 battery cell from an experimental dataset obtained in the University of Oviedo Battery Laboratory.

TL;DR: A hybrid method in which sound companies are divided in clusters using Self Organized Maps and then each cluster is replaced by a director vector which summarizes all of them and the results show that the proposed hybrid approach is much more accurate than the benchmark techniques for the identification of the bankrupt companies.