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.
|Konference||21st IEEE International Conference on Mobile Data Management, MDM 2020|
|Periode||30/06/2020 → 03/07/2020|
|Sponsor||IEEE, IEEE Computer Society TCDE|
|Navn||Proceedings - IEEE International Conference on Mobile Data Management|