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Times series retrieval through unsupervised learning

Grant number: 22/01359-1
Support Opportunities:Scholarships in Brazil - Master
Effective date (Start): April 01, 2022
Effective date (End): March 31, 2024
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Daniel Carlos Guimarães Pedronette
Grantee:Bionda Rozin
Host Institution: Instituto de Geociências e Ciências Exatas (IGCE). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil
Associated research grant:18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks, AP.JP2
Associated scholarship(s):23/08087-0 - Graph-based and Transfer Learning Models for Times Series Representation, BE.EP.MS


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)

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