the ability of artificial neural network (ann) for estimating the natural gas demand load for the next day and month of the populated cities has shown to be a real  concern. as the most applicable network, the ann with multi-layer back propagation perceptrons is used to approximate functions. throughout the current work, the daily effective temperature is determined, and then the weather data w...

The Prevention and Incidence of Asthma and Mite Allergy (PIAMA) study is a birth cohort study that investigates the influence of allergen exposure on the development of allergy and asthma in the first several years of life. The objectives of this study were to investigate the relationship between a family history of allergy and/or asthma and exposure of newborn children to mite and pet allergen...

Tourism forecasting plays an important role in tourism planning and management. Various forecasting techniques have been developed and applied to the tourism context, amongst which econometric forecasting has been winning an increasing popularity in tourism research. This paper therefore aims to introduce the latest developments of econometric forecasting approaches and their applications to to...

This paper presents the applicability of artificial neural networks for 24 hour ahead solar power generation forecasting of a 20 kW photovoltaic system, the developed forecasting is suitable for a reliable Microgrid energy management. In total four neural networks were proposed, namely: multi-layred perceptron, radial basis function, recurrent and a neural network ensemble consisting in ensembl...

We consider modeling a time series of smooth curves and develop methods for forecasting such curves and dynamically updating the forecasts. The research problem is motivated by efficient operations management of telephone customer service centers, where forecasts of daily call arrival rate profiles are needed for service agent staffing and scheduling purposes. Our methodology has three componen...

In this study, a novel selective & nonlinear neural network ensemble model, i.e. NSNNEIPCABag, is proposed for economic forecasting. In this model, some different training subsets are first generated by bagging algorithm. Then the feature extraction technique, improved principal component analysis (IPCA), and then the IPCA approach is also used to extract their data features to train individual...

In this study, a reliability-based RBF neural network ensemble forecasting model is proposed to overcome the shortcomings of the existing neural ensemble methods and ameliorate forecasting performance. In this model, the ensemble weights are determined by the reliability measure of RBF network output. For testing purposes, we compare the new ensemble model’s performance with some existing netwo...

In order to improve the accuracy of power load forecasting, this paper proposes a neural network-based short-term monitoring method. First, original energy signal is decomposed by CEEMDAN algorithm obtain several eigenmode function components and residual components; functions are fed into NARX network for computational purposes. The partial hypothesis superimposed in following part final forec...

The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis of NSM parameters and their temporal an...

This paper focuses on the study of short term load forecasting (STELF) using interval Type-2 Fuzzy Logic (IT2FL) and feed-forward Neural Network with back-propagation (NN-BP) tuning algorithm to improve their approximation capability, flexibility and adaptiveness. IT2FL for STELF is presented which provides additional degrees of freedom for handling more uncertainties for improving prediction a...