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

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

Description

Title: Event-based time series data preprocessing: application to traffic flow time series
Author/s:
  • Zhu, B.
  • Pérez Pérez, Aurora
  • Caraça-Valente Hernández, Juan Pedro
Item Type: Presentation at Congress or Conference (Other)
Event Title: ITISE 2014, International Work-Conference on Time Series
Event Dates: 25-27 Jun 2014
Event Location: Granada, España
Title of Book: Proceedings ITISE 2014: International Work-Conference on Time Series
Date: 2014
ISBN: 978-84-15814-97-
Volume: 2
Subjects:
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 36830
DC Identifier: http://oa.upm.es/36830/
OAI Identifier: oai:oa.upm.es:36830
Deposited by: Memoria Investigacion
Deposited on: 21 Sep 2015 09:30
Last Modified: 06 Jun 2016 09:30
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