@conference {17360,
	title = {Similarity-Based Forecasting with Simultaneous Previews: A River Plot Interface for Time Series Forecasting},
	booktitle = {Information Visualization, 2007. IV {\textquoteright}07. 11th International Conference},
	year = {2007},
	month = {2007/07/04/6},
	pages = {191 - 196},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Time-series forecasting has a large number of applications. Users with a partial time series for auctions, new stock offerings, or industrial processes desire estimates of the future behavior. We present a data driven forecasting method and interface called similarity-based forecasting (SBF). A pattern matching search in an historical time series dataset produces a subset of curves similar to the partial time series. The forecast is displayed graphically as a river plot showing statistical information about the SBF subset. A forecasting preview interface allows users to interactively explore alternative pattern matching parameters and see multiple forecasts simultaneously. User testing with 8 users demonstrated advantages and led to improvements.},
	keywords = {data driven forecasting method, data visualisation, Data visualization, Economic forecasting, forecasting preview interface, Graphical user interfaces, historical time series dataset, Laboratories, new stock offerings, partial time series, pattern matching, pattern matching search, Predictive models, river plot interface, Rivers, similarity-based forecasting, Smoothing methods, Technological innovation, Testing, time series, time series forecasting, Weather forecasting},
	isbn = {0-7695-2900-3},
	doi = {10.1109/IV.2007.101},
	author = {Buono,P. and Plaisant, Catherine and Simeone,A. and Aris,A. and Shneiderman, Ben and Shmueli,G. and Jank,W.}
}
@conference {17535,
	title = {Predicting the CPU availability of time-shared Unix systems on the computational grid},
	booktitle = {The Eighth International Symposium on High Performance Distributed Computing, 1999. Proceedings},
	year = {1999},
	month = {1999///},
	pages = {105 - 112},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Focuses on the problem of making short- and medium-term forecasts of CPU availability on time-shared Unix systems. We evaluate the accuracy with which availability can be measured using the Unix load average, the Unix utility {\textquotedblleft}vmstat{\textquotedblright} and the Network Weather Service (NWS) CPU sensor that uses both. We also examine the autocorrelation between successive CPU measurements to determine their degree of self-similarity. While our observations show a long-range autocorrelation dependence, we demonstrate how this dependence manifests itself in the short- and medium-term predictability of the CPU resources in our study},
	keywords = {accuracy, Application software, Autocorrelation, Availability, Central Processing Unit, computational grid, correlation methods, CPU availability prediction, CPU resources predictability, CPU sensor, Dynamic scheduling, grid computing, Load forecasting, long-range autocorrelation dependence, medium-term forecasts, network operating systems, Network Weather Service, NWS, performance evaluation, self-similarity degree, short-term forecasts, successive CPU measurements, Time measurement, Time sharing computer systems, time-shared Unix systems, time-sharing systems, Unix, Unix load average, vmstat utility, Weather forecasting},
	isbn = {0-7803-5681-0},
	doi = {10.1109/HPDC.1999.805288},
	author = {Wolski,R. and Spring, Neil and Hayes,J.}
}
