Antonis Alexandridis

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Wavelet networks (WNs) are a new class of networks which have been used with great success in a wide range of applications. However a general accepted framework for applying WNs is missing from the literature. In this study, we present a complete statistical model identification framework in order to apply WNs in various applications. The following subjects(More)
In this paper, we use neural networks in order to model the seasonal component of the residual variance of a mean-reverting Ornstein-Uhlenbeck temperature process, with seasonality in the level and volatility. We also use wavelet analysis to identify the seasonality component in the temperature process as well as in the volatility of the temperature(More)
—Rainfall is one of the most challenging variables to predict, as it exhibits very unique characteristics that do not exist in other time series data. Moreover, rainfall is a major component and is essential for applications that surround water resource planning. In particular, this paper is interested in the prediction of rainfall for rainfall derivatives.(More)
The scope of this study is to present a complete statistical framework for model identification of wavelet neural networks (WN). In each step in WN construction we test various methods already proposed in literature. In the first part we compare four different methods for the initialization and construction of the WN. Next various information criteria as(More)
The increasing demand for easily accessible cash drives banks to expand their Automatic Teller Machine networks. As the network increase it becomes more difficult to supervise it while the operating costs rise significantly. Cash demand needs to be forecasted accurately so that banks can avoid storing extra cash money and can profit by mobilizing the idle(More)
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