This research presents a comparative analysis of the wind speed forecasting accuracy of univariate and multivariate ARIMA models with their recurrent neural network counterparts. The analysis utilizes contemporaneous wind speed time histories taken from the same tower location at five different...

In this position paper we discuss how language influences the mind by comparing robots that have language with robots that do not have language. Robots with language respond more adaptively to objects belonging to different categories and requiring different behaviors compared to robots without...

One of the key problems in database marketing is the identification and profiling of households that are most likely to be interested in a particular product or service. Principal component analysis (PCA) of customer background information followed by logistic regression analysis of response...

Predicting job completion time is an important but difficult task to a semiconductor fabrication plant. To further enhance its effectiveness, an evolving hybrid neural approach is proposed in this study. To evaluate the effectiveness of the proposed approach, Production Simulation (PS) is also...

Business sectors ranging from banking and insurance to retail, are benefiting from a whole new generation of 'intelligent' computing techniques. Successful applications include asset forecasting, credit evaluation, fraud detection, portfolio optimization, customer profiling, risk assessment,...

This paper presents a comparison of various forecasting approaches, using time series analysis, on mean hourly wind speed data. In addition to the traditional linear (ARMA) models and the commonly used feed forward and recurrent neural networks, other approaches are also examined including the...

This research addresses an important problem in finance, which is the portfolio selection and management problem. Modern portfolio theory and Markowitz efficient frontier start with a set of assets (securities) and generate an optimal weight combination for the optimal risky portfolio that lies...

In this paper, a novel hybrid intelligent framework is developed by applying a systematic integration of group method of data handling (GMDH) neural networks with genetic algorithm and rule-based expert system with web-based text mining for crude oil price forecasting. Our research reveals that...

This paper presents a novel method for the forecasting of mean hourly wind speed data using time series analysis. The initial point for this approach is mainly the fact that none of the forecasting approaches for hourly data, that can be found in the literature, based on time series analysis or...

To obtain, over medium term periods, wind speed time series on a site, located in the southern part of the Paris region (France), where long recording are not available, but where nearby meteorological stations provide large series of data, use was made of ANN based models. The performance of...