Comparative study of complexity measures for time series classification and cluste...
Time series classification algorithms applied to embedded systems
Incorporating the semantics into the websensors construction process
Grant number: | 12/07295-3 |
Support type: | Regular Research Grants |
Duration: | July 01, 2012 - June 30, 2014 |
Field of knowledge: | Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques |
Principal researcher: | Gustavo Enrique de Almeida Prado Alves Batista |
Grantee: | Gustavo Enrique de Almeida Prado Alves Batista |
Home Institution: | Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil |
Assoc. researchers: | Eamonn John Keogh ; Ronaldo Cristiano Prati ; Solange Oliveira Rezende |
Associated grant(s): | 13/50379-6 - Research on geo-spatial marine biology data mining using time series, text mining and visualization, AP.R |
Abstract
Recently, there is an increasing interest in time series processing due to the large number of application domains that generate data with such property. Such interest can be measured by the vast amount of methods recently proposed in literature to tasks such as classification, clustering, summarization, abnormality detection and motif discovery. Recent studies have shown for several problems that methods based on similarity present an efficacy that is hardly surpassed, even when compared to more sophisticated methods. This is mainly due to the fact that the community has studied and proposed several invariances to distance measures for time series. The invariances make the distance measures ignore certain undesired data properties. The most well-known example is the invariance to local differences in time scale, obtained with the warping technique. Other invariances include the invariance to differences in amplitude and offset, phase and occlusion. Recently, we demonstrated to the scientific community that time series similarity classification methods can be largely benefited by a new invariance: complexity invariance. The main objective of this research project is to investigate new complexity-invariant distance measures and assess how such measures can improve the efficacy especially of clustering and motif discovery algorithms. (AU)
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