Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Mining Deviants in a Time Series Database
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
On map-matching vehicle tracking data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Similarity search over time series and trajectory data
Similarity search over time series and trajectory data
A conceptual view on trajectories
Data & Knowledge Engineering
On the effect of trajectory compression in spatiotemporal querying
ADBIS'07 Proceedings of the 11th East European conference on Advances in databases and information systems
A hybrid model and computing platform for spatio-semantic trajectories
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part I
Semantic trajectories: Mobility data computation and annotation
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
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Trajectory data play an important role in analyzing real world applications that involve movement features, e.g. natural and social phenomena such as bird migration, transportation management, urban planning and tourism analysis. Such trajectory data are a speical kind of time series with another focus on the spatial dimension besides the temporal one. Traditional time series models, especially the ARIMA (Auto-Regression Integrated Moving Average) model, have provided sound theoretical backgrounds and promoted many successful applications for managing and forecasting time-relevant sequential data. This paper aims at extending the ARIMA model with spatial dimension, and further applying it for the network-constrained trajectory data. We implement and evaluate the model for trajectory database, in the context of traffic application scenario about vehicle movement constrained under a given network infrastructure. The proposed Traj-ARIMA model has many application perspectives, such as trajectory data regression and compression, outliers detection, traffic flow and vehicle speed prediction. In this paper, the major focus is on vehicle speed forecasting.