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Electric energy plays a vital role in the achievement of social, economic and environment development of any nation. Thus, efficient demand planning and production of energy is needed to avoid too much over/under-estimation of electric load. In this study, the researchers proposed a scheme with eight steps for a dynamic time series forecasting using adaptive multilayer perceptron with minimal complexity...
A prediction scheme for sunspot series using a Recurrent Neural Network is proposed in this paper. The recurrent neural network adopted in this scheme is the Bilinear recurrent neural network (BRNN). Since the BRNN is based on the bilinear polynomial, BRNN has been successfully used in modeling highly nonlinear systems with time-series characteristics. Dynamic-BRNN (D-BRNN) further improves the convergence...
Research on time series forecasting has been an area of considerable interest in recent decades. Several techniques have been researched for time series forecasting. There is a fundamental task in any area of knowledge of time series: use past values to predict future values from the available historical series. Thus, a very important step is to define which of these past values will be considered...
Salmonellosis is one of the most common seasonal zoonosis. As from the definition, zoonosis refers to the transmission of infectious diseases from animal to human. This paper presents the prediction of Salmonellosis incidence using Artificial Neural Network (ANN) on the basis of monthly data. A series of Salmonellosis incidence in US from 1993 to 2006, published by Centers for Disease Control and...
This paper presents a novel application of the self-organised multilayer perceptrons inspired by the immune algorithm in financial time series prediction. The simulation results were compared with the multilayer perceptrons and the functional link neural networks. The prediction capability of the various neural networks was tested on ten different data sets; the US/UK exchange rates, the JP/US exchange...
This paper presents the prediction of financial time series using an adaptive neural network, which is called the self-organised multilayer perceptrons inspired by the immune algorithm. The simulation results were compared with the multilayer perceptrons and the functional link neural networks. The prediction capability of the various neural networks was tested on ten different data sets; the US/UK...
The success of strategic and detailed planning of public transportation highly depends on accurate demand information data. Short-term forecasting is the key to the success of transportation operations planning such as time-tabling and seat allocation. This study adopts neural networks to develop short-term passenger demand forecasting models to be used in operational management of light rail services...
This work presents an award winning approach for solving the NN3 forecasting competition problem. It consisted of predicting 18 future values of 111 monthly short time series. This approach consists of applying the median value of a 15-MLP ensemble for predicting each time series. The system performed very well on test data, finishing as the second best solution of the competition with a SMAPE=16...
In this paper, a focused time lagged recurrent neural network (FTLRNN) model with gamma memory is developed for multi step ahead (k=1,5,10.20,50,100) prediction of typical Duffing Chaotic time series. It is popularized in Neural network field due to its richness in chaotic behavior. It is observed that duffing time series exhibit a rich chaotic behavior. This paper compares the performance of two...
This work introduces a quantum-inspired intelligent hybrid (QIIH) method for stock market forecasting. It performs a quantum-inspired evolutionary search for the minimum necessary dimension (time lags) embedded in the problem for determining the characteristic phase space that generates the financial time series phenomenon. The proposed QIIH method consists of a quantum-inspired intelligent hybrid...
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