Forecasting Evolving Curves

Sivan Aldor-Noiman, University of Pennsylvania

Abstract

This dissertation describes methodologies for forecasting and testing integer valued time series that consist of Poisson counts. In Chapter 2 we look at univariate time series in which the counts are large and can be approximated using Gaussian models. These models are motivated by data gathered from the call center of a large financial institute. The goal of the models in this application is to predict accurately the one-day-ahead arrival process of incoming calls to the call center. A secondary goal is to provide a Bayesian algorithm to dynamically update these forecasts as more data becomes available. To test the underlying Gaussian assumption we develop in Chapter 1 a simple new graphical procedure which provides confidence bands for a normal quantile-quantile plot. These bands define a test of normality which is both a powerful and visually insightful tool compared to the common used testing procedures. In Chapter 3 we develop a Bayesian nonparametric model for multivariate Poisson time series which have small counts. We demonstrate this model using simulations and by forecasting violent crimes pattern across Washington D.C.^

Subject Area

Statistics

Recommended Citation

Sivan Aldor-Noiman, "Forecasting Evolving Curves" (January 1, 2012). Dissertations available from ProQuest. Paper AAI3508962.
http://repository.upenn.edu/dissertations/AAI3508962

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