Soft computing for intelligent data analysis

Intelligent data analysis (IDA) is an interdisciplinary study concerned with the effective analysis of data. The paper briefly looks at some of the key issues in intelligent data analysis, discusses the opportunities for soft computing in this context, and presents several IDA case studies in which soft computing has played key roles. These studies are all concerned with complex real-world problem solving, including consistency checking between mass spectral data with proposed chemical structures, screening for glaucoma and other eye diseases, forecasting of visual field deterioration, and diagnosis in an oil refinery involving multivariate time series. Bayesian networks, evolutionary computation, neural networks, and machine learning in general are some of those soft computing techniques effectively used in these studies

Forecasting meteorological time series using soft computing methods: an empirical study

Abstract: The interest of researchers in different fields of science towards modern soft computing data driven methods for time series forecasting has grown in recent years. Modeling and forecasting hydrometeorological variables is an important step in understanding climate change. The application of modern methods instead of traditional statistical techniques has lead to great improvement in past studies on meteorological time series. In this paper, we employ Support Vector Regression (SVR) and automatic model induction by means of Adaptive Gene Expression Programming (AdaGEP) for modeling and short term forecasting of real world hydrometeorological time series. The investigated time series datasets cover annual, respectively monthly data, on temperature and precipitation, measured at several meteorological stations in the Black Sea region. Two performance measures were used to assess the efficiency of the models obtained for forecasting, alongside statistical testing of the goodness of fit via the Kolmogorov-Smirnov test. Based on the results of rigourous experiments, we conclude that the models obtained by the AdaGEP algorithm are more competent in forecasting the time series considered in this paper than the models produced with the SVR algorithm

Soft computing techniques applied to finance

Soft computing is progressively gaining presence in the financial world. The number of real and potential applications is very large and, accordingly, so is the presence of applied research papers in the literature. The aim of this paper is both to present relevant application areas, and to serve as an introduction to the subject. This paper provides arguments that justify the growing interest in these techniques among the financial community and introduces domains of application such as stock and currency market prediction, trading, portfolio management, credit scoring or financial distress prediction areas.Publicad