Outputs (2)

Event series prediction via non-homogeneous Poisson process modelling (2016)
Presentation / Conference Contribution
Goulding, J., Preston, S. P., & Smith, G. (2016, December). Event series prediction via non-homogeneous Poisson process modelling. Presented at 2016 IEEE International Conference on Data Mining (ICDM), Barcelona, Spain

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)
Presentation / Conference Contribution
Barrack, D. S., Goulding, J., Hopcraft, K., Preston, S., & Smith, G. AMP: a new time-frequency feature extraction method for intermittent time-series data. Presented at 1st International Workshop on Mining and Learning from Time Series (MiLeTS)

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.