All Outputs (2)

Event series prediction via non-homogeneous Poisson process modelling (2016)
Conference Proceeding
Goulding, J., Preston, S. P., & Smith, G. (2016). Event series prediction via non-homogeneous Poisson process modelling. In 2016 IEEE 16th International Conference on Data Mining (ICDM). https://doi.org/10.1109/ICDM.2016.0027

Data streams whose events occur at random arrival times rather than at the regular, tick-tock intervals of traditional time series are increasingly prevalent. Event series are continuous, irregular and often highly sparse, differing greatly in nature... Read More about Event series prediction via non-homogeneous Poisson process modelling.

AMP: a new time-frequency feature extraction method for intermittent time-series data (2015)
Conference Proceeding
Barrack, D. S., Goulding, J., Hopcraft, K., Preston, S., & Smith, G. (2015). AMP: a new time-frequency feature extraction method for intermittent time-series data.

The characterisation of time-series data via their most salient features is extremely important in a range of machine learning task, not least of all with regards to classification and clustering. While there exist many feature extraction techniques... Read More about AMP: a new time-frequency feature extraction method for intermittent time-series data.