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Introduction

  • Monidipa DasEmail author
  • Soumya K. Ghosh
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 858)

Abstract

Spatial time series prediction is a major family of spatio-temporal data mining and it has huge application in various domains, including environmental management, transportation, epidemiology, climatology, and so on. With the exponentially increasing volume of spatial data  during last few decades, there has been a growing research interest with a prominent attention on devising probabilistic graph -based approaches in this regard. The present monograph is a modest attempt to provide a compact study on the recent development of Bayesian network -based models for spatial time series prediction . The entire monograph is comprised of nine chapters. This chapter introduces the basics of spatial time series prediction followed by the relevant challenges and the state-of-the-art techniques to handle the same. Subsequently, the chapter discusses on the research gap due to the difficulty faced by research beginners to get a unified view of evolution of the relevant research from the scattered literature and eventually this is identified as the source of motivation behind this monograph. Finally, the chapter ends with a section outlining the overall structure of the remainder of the monograph.

Keywords

Spatial data Spatio-temporal data Spatial time series Prediction Statistical techniques Computational intelligence 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology KharagpurKharagpurIndia

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