River stage forecasting is an important issue in water resources management and real-time prediction of extreme floods. The present study investigates the performance of the wavelet regression (WR) technique in daily river stage forecasting. The WR model was improved combining two methods,...

Using a panel of 439 German regions, we evaluate and compare the performance of various Neural Network (NN) models as forecasting tools for regional employment growth. Because of relevant differences in data availability between the former East and West Germany, the NN models are computed...

Statistical Learning refers to statistical aspects of automated extraction of regularities (structure) in datasets. It is a broad area which includes neural networks, regression-trees, nonparametric statistics and sieve approximation, boosting, mixtures of models, computational complexity,...

This paper presents a novel method for the forecasting of mean hourly wind speed data using time series analysis. The initial point for this approach is mainly the fact that none of the forecasting approaches for hourly data, that can be found in the literature, based on time series analysis or...

The increasing interest aroused by more advanced forecasting techniques, together with the requirement for more accurate forecasts of tourism demand at the destination level due to the constant growth of world tourism, has lead us to evaluate the forecasting performance of neural modelling...

In this paper, alternative non-parametric forecasting techniques are analysed, with emphasis placed on the difference between the reconstruction and learning approaches. The former is based on Takens’ Theorem, which recovers unknown dynamic properties of a system; it is appropriate in...

This paper relies on wavelet multiresolution analysis to investigate the dependence structure and predictability of currency markets across different timescales. It explores the nature and direction of causality among the exchange rates with respect to the US dollar of the most widely traded...

Neural network models were compared to traditional forecasting methods in forecasting the quarterly and monthly farm price of hogs. A quarterly neural network model forecasted poorly in comparison to a quarterly econometric model. A monthly neural network model outperformed a monthly ARIMA model...

There has been an increased number of papers in the literature in recent years, applying several methods and techniques for exchange - rate prediction. This paper focuses on the Greek drachma using daily observations of the drachma rates against four major currencies, namely the U.S. Dollar...

Financial forecasting is a difficult task due to the intrinsic complexity of the financial system. A simplified approach in forecasting is given by "black box" methods like neural networks that assume little about the structure of the economy. In the present paper we relate our experience using...