Data streams whose events occur at random arrival times rather than at the regular, tick-tock intervals of traditional time series are highly prevalent, particularly in human behavioural data. Continuous, irregular and often highly sparse, these event series differ greatly to traditional, regularly sampled, time series and traditional approaches to analysis tend to perform poorly. This focus of this project is the development of novel techniques addressing this. Two examples are the extraction of underlying time-frequency patterns in highly intermittent data and the prediction of non-stationary, intermittent time series.
Project output: Event Series Prediction via Non-homogeneous Poisson Process Modelling, Goulding, J, Preston, S., Barrack, D., Smith, G. IEEE International Conference on Data Mining, Barcelona, Spain (ICDM), 2016. pre-print IEEE Xplore