Mankind is confronted with an increasingly complex system, especially the major decision issues, such as organization and management, coordination, planning, forecasting, controlling and so on. Due to the complexity of the system, many fuzzy factors and the limitations of the existing complex giant system theory, there are still many problems to be solved. The research on the giant complex syst...

Using five years of data collected from a small and independent hotel in The Netherlands this case study explores RMS data as a means to seek new insights into occupancy forecasting. The study provides an insight into the random nature of group cancellations, an important but neglected aspect in hotel revenue management modeling. The empirical study also shows that in a local market context dem...

The aviation industry represents a complex system with low-volume high-value manufacturing, long lead times, large capital investments, and highly variable demand. Making important decisions with intensive capital investments requires accurate forecasting of future demand. However, this can be challenging because of significant variability in future scenarios. The purpose of this research is to...

Abstract This paper explores forecasting using model selection and model averaging and attempts to draw conclusion both in the context of stationarity and non-stationarity. Model averaging tends to be viewed as a polar opposite of model selection; often the motivation for averaging is to avoid the pitfalls of selecting models. However, selection cannot be avoided since every possible model cann...

We study some methods of combining procedures for forecasting a continuous random variable. Statistical risk bounds under the square error loss are obtained under mild distributional assumptions on the future given the current outside information and the past observations. The risk bounds show that the combined forecast automatically achieves the best performance among the candidate procedures ...

While artificial neural networks (NN) promise superior performance in forecasting theory, they are not an established method in business practice. The vast degrees of freedom in modeling NNs have lead to countless publications on heuristic approaches to simplify modeling, training, network selection and evaluation. However, not all studies have conducted experiments with the same scientific rig...

A forecasting model of new product demand has been developed and applied to forecast new vehicle demand in Malaysia. Since the publication of the Bass model in 1969, innovation of new diffusion theory has sparked considerable research among marketing science scholars, operational researchers and mathematicians. This study considers the Bass Model for forecasting the diffusion of new products or...

Fuzzy time series forecasting methods have been widely studied in recent years. This is because fuzzy time series forecasting methods are compatib le with flexib le calculat ion techniques and they do not require constraints that exist in conventional time series approaches. Most of the real life time series exh ibit periodical changes arising from seasonality. These variations are called seaso...

Proper characterization of uncertainty is a challenging task. Depending on the sources of uncertainty, various uncertainty modeling frameworks have been proposed and studied in the uncertainty quantification literature. This paper applies various uncertainty modeling frameworks, namely possibility theory, Dempster-Shafer theory and probability theory to isosurface extraction from uncertain scal...

We extend the projective covariant bookmaker’s bets model to the forecasting gamblers case. The probability of correctness of forecasts shifts probabilities of branching. The formula for the shift of probabilities leads to the velocity addition rule of the special theory of relativity. In the absence of information about bookmaker’s wagers the stochastic logarithmic rates completely determines ...