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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...
Persistent link: https://www.econbiz.de/10009429663
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...
Persistent link: https://www.econbiz.de/10009429664
In this paper we provide experimental results and extensions to our previous theoretical findings concerning the combination of forecasts that have been diversified by three different methods: with parameters learned at different data aggregation levels, by thick modeling and by the use of...
Persistent link: https://www.econbiz.de/10009429665
The combination of forecasts is a well established procedure for improving forecast performance and decreasing the risk of selecting an inferior model out of an existing pool of models. Work in this area mainly focuses on combining several functionally different models, but some publications...
Persistent link: https://www.econbiz.de/10009429719
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...
Persistent link: https://www.econbiz.de/10009429780
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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...
Persistent link: https://www.econbiz.de/10009429842
The parameter in negative correlation learning (NC) controls the degree of co-operation between individual networks. This paper looks at the way the choice of in the NC algorithm affects the complexity of the function NC can fit, and shows that it acts as a complexity control allowing smooth...
Persistent link: https://www.econbiz.de/10009429666