Interval-valued time series are interval-valued data that are collected in a chronological sequence over time. This paper introduces three approaches to forecasting interval-valued time series. The first two approaches are based on multilayer perceptron (MLP) neural networks and Holt’s...

A fully connected set of formal neurons that has not been subject to any training algorithm is studied. The thresholds and couplings are random variables chosen from Gaussian distributions. The dynamics of the model can be studied within a mean field approximation. Our results show a change of...

The design process of photovoltaic (PV) modules can be greatly enhanced by using advanced and accurate models in order to predict accurately their electrical output behavior. The main aim of this paper is to investigate the application of an advanced neural network based model of a module to...

Neural networks (NNs) are one of the most widely used techniques in literature for forecasting electricity prices. However, nonzero forecast errors always occur, no matter what explanatory variables, NN types, or training methods are used in experiments. Persistent forecasting errors warrant the...

The purpose of this research is to investigate dividend policy, including its impact on share prices of transportation providers and related service companies, by comparing generalized regression neural networks with conventional regressions. Our results using regressions reveal that for Europe...

In the recent years, by rapid growth of wind power generation in addition to its high penetration in power systems, the wind power prediction has been known as an important research issue. Wind power has a complicated dynamic for modeling and prediction. In this paper, different hybrid...

From the products bought, by basket analysis we seek to infer interest, values and choice criteria and predict purchase probabilities for other products. This statistical approach relies on the existence of a few general under-lying clusters which enables the prediction of general and specific...

The hedonic price method is well adapted to the calculation of relative prices and the estimation of the quality price relationship for a complex product. The main weakness lies in the use of multiple regression for the evaluation of the coefficients when there is very little data and when the...

The ability to identify likely takeover targets at an early stage should provide investors with valuable information, enabling them to profit by investing in potential target firms. In this paper we contribute to the takeover forecasting literature by suggesting the combination of probability...

The objective of this paper is to compare different forecasting methods for the short run forecasting of Business Survey Indicators. We compare the forecasting accuracy of Artificial Neural Networks (ANN) vs. three different time series models: autoregressions (AR), autoregressive integrated...