We develop forecasting models based on the neural approach for the forecasting of the bond price time series provided by the VUB bank and make their comparisons of the forecast accuracy with the class of the statistical ARCH-GARCH models. There is a limited statistical or computer science theory on how to design the architecture of the RBF networks for some specific nonlinear financial or econo...

In this work, a two-dimensional (2-D) representation of the hourly solar radiation data is proposed. The model enables accurate forecasting using image prediction methods. One year solar radiation data that is acquired and collected between August 1, 2005 and July 30, 2006 in Iki Eylul campus of Anadolu University, and a 2-D representation is formed to construct an image data. The data is in ra...

Accurate and reliable streamflow forecasting plays an important role in various aspects of water resources management such as reservoir scheduling and water supply. This paper shows the development of a novel hybrid model for streamflow forecasting and demonstrates its efficiency. In the proposed hybrid model for streamflow forecasting, the empirical wavelet transform (EWT) is firstly employed ...

Sensitivity analysis is a fundamental approach to identify the most significant and sensitive parameters, helping us to understand complex hydrological models, particularly for time-consuming distributed flood forecasting models based on complicated theory with numerous parameters. Based on Sobol’ method, this study compared the sensitivity and interactions of distributed flood forecasting mode...

Approaches presented today in the scientific literature suggest that there are no methodological solutions based on training of artificial neural networks to predict direction industrial development, taking into account a set factors—innovation, environmental friendliness, modernization and production growth. The aim study is develop predictive model performance management innovative systems by...

Existing malware propagation models mainly concentrate on malware epidemic consequences modeling, i.e. forecasting the number of infected computers, and are based only on current malware propagation strategies. In this article we propose a genetic algorithm based model, which aims at evaluating existing as well as modeling other potentially dangerous Internet worms’ propagation strategies. The ...

Our ltsa package implements the Durbin-Levinson and Trench algorithms and provides a general approach to the problems of fitting, forecasting and simulating linear time series models as well as fitting regression models with linear time series errors. For computational efficiency both algorithms are implemented in C and interfaced to R. Examples are given which illustrate the efficiency and acc...

This study proposed a method of energy anomaly detection by using data integration and forecasting techniques to improve energy efficiency in tire curing. Proposed method integrates energy consumption with different factors (environments, equipments, operators, tire blanks and tire types). Artificial neural network model and Support Vector Machine model were used to forecast normal interval for...

We study the autocorrelation structure and the spectral density function of aggregates from a discrete-time process. The underlying discrete-time process is assumed to be a stationary AutoRegressive Fractionally Integrated MovingAverage (ARFIMA) process, after suitable number of differencing if necessary. We derive closed-form expressions for the limiting autocorrelation function and the normal...