Change Point Detection for Integer Time Series

In the 21st century, huge amounts of data are collected - and many present themselves as so-called integer data. These usually occur when things are counted: How many times is a stock traded per day? How many times is a website accessed per hour? How many different characters do emails contain?

New Insights Through Change Point Detection

By searching for change points, new information about such data can be found, automated and without human bias. In clinical trials, effective drugs should lead to improvement in measured symptoms after the start of intake, but an ineffective drug should not. In case of improvement, the structure of the time series changes after intake and such a point in time is called a change point.

Abrupt and Continuous Changes

An exemplary case for a clinical trial of an integer problem is a drug against seizures in epilepsy patients. In this case, seizures can be expected to decrease from one observation to the next - i.e., abruptly - to a lower level. In other cases, however, a change point may mark a continuous change from one level to the next. When developing case rates of infectious diseases, one can model an outbreak as a change point. However, new techniques are needed to recognize an exponential development as a change point.

Poisson INGARCH(1) Model with Logistic Intensity

The focus of the work is on the detection of such continuous change points. For time series with abrupt change points there are already methods to find them. However, this does not guarantee the search for continuous change points. A model for integer time series is the so-called Poisson INGARCH(1) model. However, it is not suitable for detecting such continuous change points. To improve this, work is being done on the intensity function of the process, which must satisfy certain contractivity properties. In the course of this work, a logistic function for the intensity is used instead, which does not necessarily satisfy these properties, while preserving properties of the model itself.