We present a case study in modeling the North Pacific (NP) index, which is a time series related to atmospheric pressure variations at sea level. We consider three statistical models, namely, a Gaussian stationary autoregressive process, a Gaussian stationary fractionally differenced (FD) process, and a ‘signal plus noise’ process consisting of a square wave oscillation with a pentadecadal peri...

We propose a method of modeling panel time series data with both inter-and intra-individual correlation, and of tting an autoregressive model to such data. Estimates are obtained by a conditional likelihood argument. If there are few observations in each series, the estimates can be dramatically improved by Burg-type estimates taking edge eeects into account. The consequences of ignoring the in...

BACKGROUND Emergency department (ED) based syndromic surveillance systems identify abnormally high visit rates that may be an early signal of a bioterrorist attack. For example, an anthrax outbreak might first be detectable as an unusual increase in the number of patients reporting to the ED with respiratory symptoms. Reliably identifying these abnormal visit patterns requires a good understand...

We address the problem of model comparison and model mixing in time series using the approach known as Hierarchical Mixtures-of-Experts. Our methodology allows for comparisons of arbitrary models, not restricted to a particular class or parametric form. Additionally, the approach is flexible enough to incorporate exogenous information that can be summarized in terms of covariables or simply tim...

The actual effort to evolve and maintain a software system is likely to vary depending on the amount of clones (i.e., duplicated or slightly different code fragments) present in the system. This paper presents a method for monitoring and predicting clones evolution across subsequent versions of a software system. Clones are firstly identified using a metricbased approach, then they are modeled ...

Categorical time series data can not be eeectively visualized and modeled using methods developed for ordinal data. The arbitrary mapping of categorical data to ordinal values can have a number of undesirable consequences. New techniques for visualizing and modeling categorical time series data are described, and examples are presented using computer and communications network traces.

Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning of the relevant patterns This dissertation prop...

Empirical time series often contain observational noise. We investigate the effect of this noise on the estimated parameters of models fitted to the data. For data of physiological tremor, i.e. a small amplitude oscillation of the outstretched hand of healthy subjects, we compare the results for a linear model that explicitly includes additional observational noise to one that ignores this nois...