Online prediction is a process that repeatedly predicts the next element in the coming period from a sequence of given previous elements. This process has a broad range of applications in various areas, such as medica...Online prediction is a process that repeatedly predicts the next element in the coming period from a sequence of given previous elements. This process has a broad range of applications in various areas, such as medical, streaming media, and finance. The greatest challenge for online prediction is that the sequence data may not have explicit features because the data is frequently updated, which means good predictions are difficult to maintain. One of the popular solutions is to make the prediction with expert advice, and the challenge is to pick the right experts with minimum cumulative loss. In this research, we use the forex trading prediction, which is a good example for online prediction, as a case study. We also propose an improved expert selection model to select a good set of forex experts by learning previously observed sequences. Our model considers not only the average mistakes made by experts, but also the average profit earned by experts, to achieve a better performance, particularly in terms of financial profit. We demonstrate the merits of our model on two real major currency pairs corpora with extensive experiments.展开更多
基金This work was supported by the Natural Science Foundation of Guangdong Province, China （2015A030310509）, the National Natural Science Foundation of China （Grant Nos. 61370229, 61272067, 61303049）, the S＆T Planning Key Projects of Guangdong Province （2014B010117007, 2015B010109003, 2015A030401087, 2016A030303055, 2016B030305004, and 2016B010109008） and the S＆T Projects of Guangzhou Municipality, China （201604010003）.
文摘Online prediction is a process that repeatedly predicts the next element in the coming period from a sequence of given previous elements. This process has a broad range of applications in various areas, such as medical, streaming media, and finance. The greatest challenge for online prediction is that the sequence data may not have explicit features because the data is frequently updated, which means good predictions are difficult to maintain. One of the popular solutions is to make the prediction with expert advice, and the challenge is to pick the right experts with minimum cumulative loss. In this research, we use the forex trading prediction, which is a good example for online prediction, as a case study. We also propose an improved expert selection model to select a good set of forex experts by learning previously observed sequences. Our model considers not only the average mistakes made by experts, but also the average profit earned by experts, to achieve a better performance, particularly in terms of financial profit. We demonstrate the merits of our model on two real major currency pairs corpora with extensive experiments.
