A Correlated Time Series Forecast System

Nicolaj Casanova Abildgaard, Casper Weiss Bang, Jonas Hansen, Tobias Lambek Jacobsen, Thomas Hojriis Knudsen, Nichlas Orts Lisby, Chenjuan Guo, Bin Yang

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

In a cyber-physical system (CPS), different entities often interact with each other across time. With the development of various sensing technologies, the time-varying interactions among entities are often recorded as multiple, correlated time series. A typical CPS is a road transportation system, where the traffic on different road segments interact with each other. Traffic sensors are often deployed to capture travel speeds on different road segments, which results in multiple, potentially correlated, speed time series. Under this setting, an increasingly pertinent task is to forecast future speeds, which is essential in a wide variety of traffic planning scenarios. We present a system for correlated time series forecast. The system is able to employ different learning algorithms to perform correlated time series forecast, which facilities end users to choose the most appropriate algorithm for their specific service. The system is developed and integrated into aSTEP, a spatio-Temporal data analytic platform developed by Aalborg University, and is tested using a wide variety of correlated time series data, including a user demand time series from a local mobility-As-A-service company.

Original languageEnglish
Title of host publicationProceedings - 2020 21st IEEE International Conference on Mobile Data Management, MDM 2020
Number of pages2
PublisherIEEE
Publication dateJun 2020
Pages242-243
Article number9162204
ISBN (Electronic)9781728146638
DOIs
Publication statusPublished - Jun 2020
Event21st IEEE International Conference on Mobile Data Management, MDM 2020 - Versailles, France
Duration: 30 Jun 20203 Jul 2020

Conference

Conference21st IEEE International Conference on Mobile Data Management, MDM 2020
CountryFrance
CityVersailles
Period30/06/202003/07/2020
SponsorIEEE, IEEE Computer Society TCDE
SeriesProceedings - IEEE International Conference on Mobile Data Management
Volume2020-June
ISSN1551-6245

Bibliographical note

Funding Information:
Acknowledgements: This work was supported by Independent Research Fund Denmark under agreements 8022-00246B and 8048-00038B.

Publisher Copyright:
© 2020 IEEE.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

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