Combining heterogeneous features for time series prediction

Publication Type:
Conference Proceeding
Proceedings of 4th International Conference on Behavioral, Economic, and Socio-Cultural Computing, BESC 2017, 2017, 2018-January pp. 1 - 2
Issue Date:
Full metadata record
© 2017 IEEE. Time series prediction is a challenging task in reality, and various methods have been proposed for it. However, only the historical series of values are exploited in most of existing methods. Therefore, the predictive models might be not effective in some cases, due to: (1) the historical series of values is not sufficient usually, and (2) features from heterogeneous sources such as the intrinsic features of data samples themselves, which could be very useful, are not take into consideration. To address these issues, we proposed a novel method in this paper which learns the predictive model based on the combination of dynamic features extracted from series of historical values and static features of data samples. To evaluate the performance of our proposed method, we compare it with linear regression and boosted trees, and the experimental results validate our method's superiority.
Please use this identifier to cite or link to this item: