Spatio-Temporal Prediction Using Data Mining Tools

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Author(s): Margaret H. Dunham (Southern Methodist University at Texas, USA), Nathaniel Ayewah (Southern Methodist University at Texas, USA), Zhigang Li (Southern Methodist University at Texas, USA), Kathryn Bean (University of Texas at Dallas, USA)and Jie Huang (University of Texas Southwestern Medical Center, USA)
Copyright: 2005
Pages: 21
Source title: Spatial Databases: Technologies, Techniques and Trends
Source Author(s)/Editor(s): Yannis Manalopoulos (Aristotle University of Thessaloniki, Greece), Apostolos Papadopoulos (Aristotle University of Thessaloniki, Greece)and Michael Gr. Vassilakopoulos (Technological Educational Institute of Thessaloniki, Greece)
DOI: 10.4018/978-1-59140-387-6.ch011

Keywords: Data Mining and Databases / Databases / Information Science Reference / Library & Information Science

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Abstract

The spatio-temporal prediction problem requires that one or more future values be predicted for time series input data obtained from sensors at multiple physical locations. Examples of this type of problem include weather prediction, flood prediction, network traffic flow, and so forth. In this chapter we provide an overview of this problem, highlighting the principles and issues that come to play in spatio-temporal prediction problems. We describe some recent work in the area of flood prediction to illustrate the use of sophisticated data mining techniques that have been examined as possible solutions. We argue the need for further data mining research to attack this difficult problem. This chapter is directed toward professionals and researchers who may wish to engage in spatio-temporal prediction.

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