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Evolutionary training for Dynamical Recurrent Neural Networks: an application in finantial time series prediction.

Miguel Delgado, M. Carmen Pegalajar, Manuel Pegalajar Cuéllar (2006)

Mathware and Soft Computing

Theoretical and experimental studies have shown that traditional training algorithms for Dynamical Recurrent Neural Networks may suffer of local optima solutions, due to the error propagation across the recurrence. In the last years, many researchers have put forward different approaches to solve this problem, most of them being based on heuristic procedures. In this paper, the training capabilities of evolutionary techniques are studied, for Dynamical Recurrent Neural Networks. The performance...

Exponential smoothing for irregular time series

Tomáš Cipra, Tomáš Hanzák (2008)

Kybernetika

The paper deals with extensions of exponential smoothing type methods for univariate time series with irregular observations. An alternative method to Wright’s modification of simple exponential smoothing based on the corresponding ARIMA process is suggested. Exponential smoothing of order m for irregular data is derived. A similar method using a DLS **discounted least squares** estimation of polynomial trend of order m is derived as well. Maximum likelihood parameters estimation for forecasting...

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