A Proof of Concept in Multivariate Time Series Clustering Using Recurrent Neural Networks and SP-Lines

• Iago Vázquez ^[1] ; Villar, José R. ^[2] ; Javier Sedano ^[1] ; Svetlana Simić ^[3] ; Cal, Enrique de la ^[2]
  1. [1] Instituto Tecnológico de Castilla y León

     Instituto Tecnológico de Castilla y León

     Burgos, España

     
  2. [2] Universidad de Oviedo

     Universidad de Oviedo

     Oviedo, España

     
  3. [3] University of Novi Sad

     University of Novi Sad

     RS.VO.6.3194359, Serbia

     

  Mostrar afiliaciones +
• Localización: Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings / coord. por Hilde Pérez García, Lidia Sánchez González, Manuel Castejón Limas, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2019, ISBN 978-3-030-29858-6, págs. 346-357
• Idioma: inglés
• Enlaces
  • Texto completo
• Resumen

  • Big Data and the IoT explosion has made clustering multivariate Time Series (TS) one of the most effervescent research fields. From Bio-informatics to Business and Management, multivariate TS are becoming more and more interesting as they allow to match events the co-occur in time but that is hardly noticeable. This study represents a step forward in our research. We firstly made use of Recurrent Neural Networks and transfer learning to analyze each example, measuring similarities between variables. All the results are finally aggregated to create an adjacency matrix that allows extracting the groups. In this second approach, splines are introduced to smooth the TS before modeling; also, this step avoid to learn from data with high variation or with noise. In the experiments, the two solutions are compared suing the same proof-of-concept experimentation.