Good performance was obtained for short forecasting horizon (H=1, or H=3), and the results were even better by favoring short input over long history, and small networks over

Input variable selection for time series forecasting with artificial neural networks - an empirical evaluation across varying time series frequencies.. Nikolaos

models for forecasting time series applied in wind generation based on the combination of time series 828. models with artificial

Load forecasting can be performed with different models like multiple regression technique [13, 26], Time series model & Artificial Neural Networks.. Time series models are

[2] Anastasiadis, A. New globally convergent training scheme based on the resilient propagation algorithm. Support vector machine with adaptive parameters in financial time

The combined prediction model, based on artificial neural networks (ANNs) with principal component analysis (PCA) for financial time series forecasting is presented in

Keywords- Electricity demand forecasting; criteria for selection; stochastic time-series; ARIMA; Exponential Smoothing; Kalman Filtering; Artificial Neural Networks; Support

Taylor Series Prediction of Time Series Data with Error Propagated by Artificial Neural NetworkS.

Artificial Neural Networks are often used for time series modeling and classification. In the last decade, advanced architectures such as deep neural networks or neural networks

Several studies for forecasting solar irradiance in different time scales have appeared recently based on time series models, Artificial Neural Networks (ANNs), Fuzzy Logic (FL)

This paper is concerned with the application of artificial neural networks (ANN) to the forecasting of the time series generated by the 10 Year Commonwealth Treasury

Keywords: combining forecasts , ensemble method , artificial neural network , stock market prediction , financial time series forecasting , exchange rate forecasting ,

The proposed network contains stacks of dilated convolutions that allow it to access a broad range of history when forecasting; multiple convolutional filters are applied in parallel

A HYBRID APPROACH BASED ON ARIMA AND ARTIFICIAL NEURAL NETWORKS FOR CRIME SERIES FORECASTING.. MOHD SUHAIMI

In this study on time series models, we analyzed and compared the Artificial Neural Networks (ANNs) and the Autoregressive Moving Averages (ARMA) in forecasting

Like slow learning feedforward ANNs, radial basis function networks typically use one input layer and one output layer but only one hidden layer.. Each hidden layer node represents

Web of Knowledge Data- base paper with the largest number of citations is Drought forecasting artificial neural using networks and time series of drought indices, by the authors

In this paper we compared the performances of different feed forward and recurrent neural networks and training algorithms for predicting the exchange rate EUR/RON and