Abstract
Predicting future calls can be the next advanced feature of the intelligent phone as the phone service providers are looking to offer new services to their customers. Call prediction can be useful to many applications such as planning daily schedule and attending unwanted communications (e.g. voice spam). Predicting calls is a very challenging task. We believe that this is a new area of research. In this paper, we propose a Call Predictor (CP) that computes the probability of receiving calls and makes call prediction based on caller’s behavior and reciprocity. The proposed call predictor is tested with the actual call logs. The experimental results show that the call predictor performs reasonably well with false positive rate of 2.4416%, false negative rate of 2.9191%, and error rate of 5.3606%.
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Magalhacs, M.H., Ballini, R., Molck, P., Gomide, F.: Combining forecasts for natural stream flow prediction. IEEE annual meeting of the fuzzy information, pp. 390–394 (2004)
Guang, C., Jian, G., Wei, D.: Nonlinear-periodical network traffic behavioral forecast based on seasonal neural network model. IEEE international conference on communications, circuits, and systems 1(1), 683–687 (2004)
Tych, W., Pedregal, D.J., Young, P.C., Davies, J.: An unobserved component model for multi-rate forecasting of telephone call demand: the design of a forecasting support system. International jouornal of forecasting 18(4), 673–695 (2002)
Hansen, J.W., Nelson, R.D.: Neural networks and traditional time series methods: a synergistic combination in state economic forecasts. IEEE Transaction on neural network 8(4), 863–873 (1997)
Eagle, N., Pentland, A., Lazer, D.: Infering social network structure using mobile phone data. In: PNAS (in submission, 2007)
Eagle, N., Pentland, A.: Reality mining: Sensing complex social systems. Personal and Ubiquitous Computing 10(4) (2006)
Eagle, N., Pentland, A.: Eigenbehaviors: Identifying structure in routine. In: Proc. Roy. Soc. A. (in submssion, 2006)
Eagle, N., Pentland, A.: Social serendipity: Mobilizing social software. IEEE Pervasive Computing 4(2) (2005)
Sheldrake, R., Smart, P.: Testing for telepathy in connection with e-mails. Perceptual and Motor Skills 10, 771–786 (2005)
Eagle, N.: Machine Perception and Learning of Complex Social Systems, PhD Thesis, Massachusetts Institute of Technology (2005)
Dantu, R., Kolan, P.: Survey of Calling Patterns. University of North Texas Internal Survey (2006), http://secnet.csci.unt.edu/nuisance/index.htm
Parzen, E.: On estimation of a probability density function and mode. Ann. Math. Statist. 3(33), 1065–1076 (1962)
Jones, M., Marron, J.S., Sheather, S.J.: A brief survey of bandwidth selection for density estimation. J. Amer. Stat. Assoc. 433(91), 401–407 (1996)
Wand, M.P., Jones, M.C.: Multivariate plug-in bandwidth selection. Computational Statistics 9, 97–117 (1994)
Sheather, S.J., Jones, C.: A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society, Series B 53, 683–690 (1991)
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© 2007 IFIP International Federation for Information Processing
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Phithakkitnukoon, S., Dantu, R. (2007). Predicting Calls – New Service for an Intelligent Phone. In: Krishnaswamy, D., Pfeifer, T., Raz, D. (eds) Real-Time Mobile Multimedia Services. MMNS 2007. Lecture Notes in Computer Science, vol 4787. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75869-3_3
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DOI: https://doi.org/10.1007/978-3-540-75869-3_3
Publisher Name: Springer, Berlin, Heidelberg
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