CLUSTERING TIME SERIES ONLINE IN A TRANSFORMED SPACE
Hamid Reza Arabnia, Junfeng Qu, Yinglei Song, Khaled Rasheed, Byron A. Jeff · 2007
Similarity-based retrieval has attracted an increas ing amount of attention in recent years. Although there are many different approaches , most are based on a common premise of dimensionality reduction and spatial acc ess methods. Relative change of the time series data provides more meaning and i nsight view of problem domain.. This paper presents our efforts on consid ering the relative changes of time series during the time series matching process . A similarity distance measure that based on transformed difference space of a ser ies of critical points is proposed. Based on experiments with financial time series dat a, it can be concluded that our distance measure works as good as the Euclidean dis tance measure based normalized data without any shifting and scaling an d PAA approach. The distance measure proposed is a general distance metric and i s suitable to deal with online similarity matching because it does not maintain st ream statistics over data streams .