Using Graph Spectral to solve Change Point Detection Problems - Uzai, Luis Gustavo C.; Kashiwabara, André Y. (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2018: Anais do XV Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2018.4461, 2018)
Time series are sequence of values distributed over time. Analyzing time series is important in many areas including medical, financial, aerospace, commercial and entertainment. Change Point Detection is the problem of identifying changes in meaning or distribution of data in a time series. This article presents Spec, a new algorithm that uses the graph spectrum to detect change points. The Spec was evaluated using the UCR Archive which is a large da- tabase of different time series. Spec performance was compared to the PELT, ECP, EDM, and gSeg algorithms. The results showed that Spec achieved a better accuracy compared to the state of the art in some specific scenarios and as efficient as in most cases evaluated.