Neural network and regression models have been developed to predict the completed cost of competitively bid highway projects constructed by the New Jersey Department of Transportation. Bid information was studied for inclusion as inputs to the models. Data studied included the low bid, median...

This study presents three modeling techniques for the prediction of electricity energy consumption. In addition to the traditional regression analysis, decision tree and neural networks are considered. Model selection is based on the square root of average squared error. In an empirical...

Estimation (Forecasting) of industrial production costs is one of the most important factor affecting decisions in the highly competitive markets. Thus, accuracy of the estimation is highly desirable. Hibrid Regression Neural Network is an approach proposed in this paper to obtain better fitness...

Regression and neural network models have been developed to predict the cost and duration of projects for the reconstruction of schools which must be quickly rebuilt. Data for the school reconstruction projects in central Taiwan, which received the most serious damage from the Chi-Chi...

The German meat market is facing considerable changes. Along with the boom of case-ready and discount stores, butchers and smaller retailers loose market shares, and private labels become widely accepted. The consumers' preferences are often neglected by these trends. This contribution discusses...

This paper proposes a methodology to jointly generate optimal forecasts from an autoregression of order p for 1 to h steps ahead. The relevant model is a Partial Least Squares Autoregression, which is positioned in between a single AR(p) model for all forecast horizons and different AR models...