A metric learning based approach to evaluate task-specific time series similarity

Authors:
Yang Lu;Wayne Xin Zhao;Hongfei Yan;Xiaoming Li
Affiliations:
Peking University, Beijing, China;Peking University, Beijing, China;Peking University, Beijing, China;Peking University, Beijing, China
Venue:
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Year:
2013

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Abstract

A variety of methods have been proposed to measure time series similarity, such as Dynamic Time Warping and Edit distance. Although these methods have been shown to be effective and useful in various data mining tasks, they seldom consider task-specific information. Without consideration of task-specific information, the similarity measures may not work quite well on specific tasks. In this paper, we investigate how to learn task-specific time series similarity measures. We adopt metric learning as the principled approach, and we proposed two novel models based on metric learning to evaluate task-specified time series similarity. We construct our test collection based on real data from Renren Games data. Extensive experimental results show that our proposed methods are very effective.