Times Series Classification Using Different Data Representations and Ensembles

• Grant number: 12/08923-8
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: August 01, 2012
• End date: January 31, 2017
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Gustavo Enrique de Almeida Prado Alves Batista
• Grantee: Rafael Giusti
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

There is an increasing interest in techniques for analysis of time-related data in the last decades. Such interest is explained by the fact that nearly all human activities produce time-related data. In this project, we aim at developing time series classification algorithms. Although a great deal of classification algorithms has been proposed in the last few decades, experimental evaluation suggests that a simple K-nearest neighbour classifier outperforms more complex classifiers in a wide range of application domains.One currently open question is how one can improve the performance of the K-nearest neighbours classifier for application domains in which this technique typically underperforms. The main hypothesis of this research work is that a simple yet effective change of data representation may provide a meaningful improvement on the classification performance. Our goal is to investigate new, effective time series data representations and propose distance measures that are effective for such representations.

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

GIUSTI, RAFAEL; SILVA, DIEGO F.; BATISTA, GUSTAVO E. A. P. A.; IEEE. Improved Time Series Classification with Representation Diversity and SVM. 2016 15TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2016), v. N/A, p. 6-pg., 2016-01-01. (13/26151-5, 12/08923-8)

SILVA, DIEGO F.; GIUSTI, RAFAEL; KEOGH, EAMONN; BATISTA, GUSTAVO E. A. P. A.. Speeding up similarity search under dynamic time warping by pruning unpromising alignments. DATA MINING AND KNOWLEDGE DISCOVERY, v. 32, n. 4, p. 988-1016, JUL 2018. (16/04986-6, 12/08923-8, 13/26151-5)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

GIUSTI, Rafael. Time series classification using multiple representations and ensembles. 2017. Doctoral Thesis - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.