Paper Info
Title
Heterogeneous Space-Time Artificial Neural Networks for Space-Time Series Prediction.
Abstract
Space-time series prediction plays a key role in the domain of geographic data mining and knowledge discovery. In general, the existing methods of space-time series prediction can be divided into two main categories: statistical machine learning methods. Comparatively, machine leaning methods have obvious advantages with respect to handling nonlinear problems. However, space-time dependence and the heterogeneity of space-time data are not well addressed by the existing machine learning methods. Because of this limitation, an accurate prediction of a space-time series is still a challenging problem. Therefore, in this study, both space-time dependence and heterogeneity are incorporated into the feedback artificial neural network, and heterogeneous space-time artificial neural networks (HSTANNs) are developed for space-time series prediction. First, to handle spatial heterogeneity, space-time series clustering is used to divide the study area into a set of homogeneous sub-areas. Then, a space-time autocorrelation analysis is employed to explore the space-time dependence structure of the dataset. Finally, a HSTANN is established for each sub-area. Further, HSTANNs are applied to predict the concentrations of fine particulate matter (PM2.5) in Beijing-Tianjin-Hebei. The experimental results show that when compared with other methods, the accuracy of the forecasting results is considerably improved by using HSTANNs.
Year
DOI
Venue
2018
10.1111/tgis.12302
TRANSACTIONS IN GIS
Field
DocType
Volume
Space time,Data mining,Computer science,Types of artificial neural networks,Artificial neural network
Journal
22.0
Issue
ISSN
Citations 
1.0
1361-1682
1
PageRank 
References 
Authors
0.34
20
6
Name
Order
Citations
PageRank
Min Deng1524.42
Wentao Yang2184.00
Qiliang Liu31229.00
Rui Jin49016.41
Feng Xu520.74
Yunfei Zhang663.44