Event-based time series data preprocessing: application to traffic flow time series

Zhu, B.; Pérez Pérez, Aurora y Caraça-Valente Hernández, Juan Pedro (2014). Event-based time series data preprocessing: application to traffic flow time series. En: "ITISE 2014, International Work-Conference on Time Series", 25-27 Jun 2014, Granada, España. ISBN 978-84-15814-97-. pp. 1268-1279.

Descripción

Título: Event-based time series data preprocessing: application to traffic flow time series
Autor/es:
  • Zhu, B.
  • Pérez Pérez, Aurora
  • Caraça-Valente Hernández, Juan Pedro
Tipo de Documento: Ponencia en Congreso o Jornada (Otro)
Título del Evento: ITISE 2014, International Work-Conference on Time Series
Fechas del Evento: 25-27 Jun 2014
Lugar del Evento: Granada, España
Título del Libro: Proceedings ITISE 2014: International Work-Conference on Time Series
Fecha: 2014
ISBN: 978-84-15814-97-
Volumen: 2
Materias:
Escuela: E.T.S. de Ingenieros Informáticos (UPM)
Departamento: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

Texto completo

[img]
Vista Previa
PDF (Document Portable Format) - Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (353kB) | Vista Previa

Resumen

Traffic flow time series data are usually high dimensional and very complex. Also they are sometimes imprecise and distorted due to data collection sensor malfunction. Additionally, events like congestion caused by traffic accidents add more uncertainty to real-time traffic conditions, making traffic flow forecasting a complicated task. This article presents a new data preprocessing method targeting multidimensional time series with a very high number of dimensions and shows its application to real traffic flow time series from the California Department of Transportation (PEMS web site). The proposed method consists of three main steps. First, based on a language for defining events in multidimensional time series, mTESL, we identify a number of types of events in time series that corresponding to either incorrect data or data with interference. Second, each event type is restored utilizing an original method that combines real observations, local forecasted values and historical data. Third, an exponential smoothing procedure is applied globally to eliminate noise interference and other random errors so as to provide good quality source data for future work.

Más información

ID de Registro: 36830
Identificador DC: http://oa.upm.es/36830/
Identificador OAI: oai:oa.upm.es:36830
Depositado por: Memoria Investigacion
Depositado el: 21 Sep 2015 09:30
Ultima Modificación: 06 Jun 2016 09:30
  • Open Access
  • Open Access
  • Sherpa-Romeo
    Compruebe si la revista anglosajona en la que ha publicado un artículo permite también su publicación en abierto.
  • Dulcinea
    Compruebe si la revista española en la que ha publicado un artículo permite también su publicación en abierto.
  • Recolecta
  • e-ciencia
  • Observatorio I+D+i UPM
  • OpenCourseWare UPM