Times series retrieval through unsupervised learning

Abstract

Many applications in science, e-commerce, and surveillance monitoring rely on recording sensor information in regular time intervals. As a consequence, time-series data has been growing tremendously. This flood of data requires data management for fast storage and access. Therefore, similarity search on time series data is a typical requirement for such databases. Besides the time series modeling, the similarity measure adopted for comparing different times series also plays an important role, directly affecting the quality of retrieval results. Traditional solutions often perform only pairwise analysis, that is, the similarity measure considers only the information given by the pairs of time series being compared. In this scenario, the relationships among objects modeled by the time series are not considered. Therefore, the intrinsic structure of the collection is ignored. In the context of this project, we intend to consider: (i) Use of unsupervised learning methods to increase the effectiveness of time series retrieval tasks by considering contextual information and relationships among data items; (ii) Comparative study considering different unsupervised learning methods for time series retrieval. (AU)