Abstract
Online prediction is a process to repeatedly predict the next element from a sequence of given previous elements. It has a broad range of applications on various areas, such as medical and finance. The biggest challenge of online prediction is sequence data does not have explicit features, which means it is difficult to remain good predictions. One of popular solution is to make prediction with expert advice, and the challenge is to pick the right experts with minimum cumulative loss. In this article, we use forex prediction as a case study, and propose a model that can select a good set of forex experts by learning a set of previous observed sequences. To achieve better performance, our model not only considers the average mistakes made by experts but also takes the average profit earn by experts into account. We demonstrate the merits of our model on a real major currency pairs data set.
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Acknowledgments
This work was supported by the Youth Teacher Startup Fund of South China Normal University (No. 14KJ18), the Natural Science Foundation of Guangdong Province, China (No. 2015A030310509), the National Science Foundation of China (61370229, 61272067, 61303049), and the S&T Projects of Guangdong Province (No. 2013B090800024, No. 2014B010103004, No. 2014B010117007, No. 2016A030303055, No. 2016B030305004, 2016B010109008).
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Zhu, J., Yang, J., Xiao, J., Huang, C., Zhao, G., Tang, Y. (2016). Online Prediction for Forex with an Optimized Experts Selection Model. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_30
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DOI: https://doi.org/10.1007/978-3-319-45814-4_30
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