@techreport{TD:100522,
	att_abstract={{Spatial-temporal models arise when data are collected across both space and time. With AT&T network data, a typical example would be that of a monitoring data on the mobility network (a network of towers) on which data are collected at regular intervals, say on a monthly basis. We have a time series associated with usage of minutes (voice) and Kb (data) for every tower located throughout the country. Thus the analysis has to take account of spatial dependence among the towers, but also that the observations at each tower typically are not independent but form a time series. In other words, one must take account of temporal correlations as well as spatial correlations. The topic of interest is how do the temporal patterns associated with the time series of a given tower correlate to temporal patterns in neighboring towers. We use a sample of time series from the network data to explore this question. 
}},
	att_authors={gs3185, cg2198, dl222g},
	att_categories={},
	att_copyright={{}},
	att_copyright_notice={{}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={spatio-temoral ,  time series ,  mobility network ,  functional data analysis },
	att_techdoc={true},
	att_techdoc_key={TD:100522},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:100522_DS1_2011-08-04T17:57:45.580Z.pdf},
	author={Ganesh Subramaniam and Colin Goodall and R. Varadhan Johns Hopkins University and Dongyu Lin},
	institution={{}},
	month={December},
	title={{Spatio-Temporal Models For Wireless Network Data }},
	year=2011,
}