使用数据挖掘工具进行时空预测

M. Dunham, N. Ayewah, Zhigang Li, Kathryn Bean, Jie Huang
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引用次数: 3

摘要

时空预测问题要求对多个物理位置的传感器获得的时间序列输入数据进行一个或多个未来值的预测。这类问题的例子包括天气预测、洪水预测、网络流量等等。在本章中,我们概述了这个问题,强调了在时空预测问题中发挥作用的原理和问题。我们描述了最近在洪水预测领域的一些工作,以说明使用复杂的数据挖掘技术,这些技术已被检查为可能的解决方案。我们认为需要进一步的数据挖掘研究来解决这个难题。本章面向可能希望从事时空预测的专业人员和研究人员。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatiotemporal Prediction using Data Mining Tools
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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