CN EN
Advanced

Financial forecasting with gompertz multiple kernel learning

Indexed by

Scopus

Abstract

Financial forecasting is the basis for budgeting activities and estimating future financing needs. Applying machine learning and data mining models to financial forecasting is both effective and efficient. Among different kinds of machine learning models, kernel methods are well accepted since they are more robust and accurate than traditional models, such as neural networks. However, learning from multiple data sources is still one of the main challenges in the financial forecasting area. In this paper, we focus on applying the multiple kernel learning models to the multiple major international stock indexes. Our experiment results indicate that applying multiple kernel learning to the financial forecasting problem suffers from both the short training period problem and non-stationary problem. Therefore we propose a novel multiple kernel learning model to address the challenge by introducing the Gompertz model and considering a non-linear combination of different kernel matrices. The experiment results show that our Gompertz multiple kernel learning model addresses the challenges and achieves better performance than the original multiple kernel learning model and single SVM models.

Keyword

Author Community

Economics/Decision Science


Related Article

Source

proceedings - ieee international conference on data mining

ISSN:1550-4786

Year:2010

Page:983-988

Language:English

Cited Count
W
Loading... 0
C
Loading...
Get Fulltext
Rights and Licenses
Related Keywords
Community
Access Stat
Creative Commons Licence
The content of CEIBS Research Online is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).