In this paper we provide a theoretical analysis of effects of applying different forecast diversification methods on the structure of the forecast error covariance matrices and decomposed forecast error components based on the bias- variance- Bayes error decomposition of James and Hastie. We...

This paper provides a discussion of the effects of different multi-level learning approaches on the resulting out of sample forecast errors in the case of difficult real-world forecasting problems with large noise terms in the training data, frequently occurring structural breaks and quickly...

In this paper a classification framework for incomplete data, based on electrostatic field model is proposed. An original approach to exploiting incomplete training data with missing features, involving extensive use of electrostatic charge analogy, has been used. The framework supports a hybrid...

Forecasting is at the heart of every revenue management system, providing necessary input to capacity control, pricing and overbooking functionalities. For airlines, the key to efficient capacity control is determining the time of when to restrict bookings in a lower-fare class to leave space...

In research of time series forecasting, a lot of uncertainty is still related to the task of selecting an appropriate forecasting method for a problem. It is not only the individual algorithms that are available in great quantities; combination approaches have been equally popular in the last...

The domain of multi level forecast combination is a challenging new domain containing a large potential for forecast improvements. This thesis presents a theoretical and experimental analysis of different types of forecast diversification on forecast error covariances and resulting combined...

Estimation of the generalization ability of a predictive model is an important issue, as it indicates expected performance on previously unseen data and is also used for model selection. Currently used generalization error estimation procedures like cross–validation (CV) or bootstrap are...

There are no algorithms that generally perform better or worse than random when looking at all possible data sets according to the no-free-lunch theorem. A specific forecasting method will hence naturally have different performances in different empirical studies. This makes it impossible to...

In this paper a classification framework for incomplete data, based on electrostatic field model is proposed. An original approach to exploiting incomplete training data with missing features, involving extensive use of electrostatic charge analogy, has been used. The framework supports a hybrid...

The Model Driven Architecture (MDA) is offered as one way forward in software systems modelling to connect software design with the business domain. The general focus of the MDA is the development of software systems by performing transformations between software design models, and the automatic...