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Spatio-Temporal Prediction Using Data Mining Tools

Spatio-Temporal Prediction Using Data Mining Tools

Margaret H. Dunham, Nathaniel Ayewah, Zhigang Li, Kathryn Bean, Jie Huang
Copyright: © 2008 |Pages: 16
ISBN13: 9781599049519|ISBN10: 1599049511|EISBN13: 9781599049526
DOI: 10.4018/978-1-59904-951-9.ch079
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MLA

Dunham, Margaret H., et al. "Spatio-Temporal Prediction Using Data Mining Tools." Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications, edited by John Wang, IGI Global, 2008, pp. 1400-1415. https://doi.org/10.4018/978-1-59904-951-9.ch079

APA

Dunham, M. H., Ayewah, N., Li, Z., Bean, K., & Huang, J. (2008). Spatio-Temporal Prediction Using Data Mining Tools. In J. Wang (Ed.), Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications (pp. 1400-1415). IGI Global. https://doi.org/10.4018/978-1-59904-951-9.ch079

Chicago

Dunham, Margaret H., et al. "Spatio-Temporal Prediction Using Data Mining Tools." In Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications, edited by John Wang, 1400-1415. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-951-9.ch079

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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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