This paper provides a description and experimental comparison of different forecast combination techniques for the application of Revenue Management forecasting for Airlines. In order to benefit from the advantages of forecasts predicting seasonal demand using different forecast models on...

Rapidly evolving businesses generate massive amounts of time-stamped data sequences and cause a demand for both univariate and multivariate time series forecasting. For such data, traditional predictive models based on autoregression are often not sufficient to capture complex non-linear...

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...

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...

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...