All Outputs (30)

Seasonal Variation in Collective Mood via Twitter Content and Medical Purchases (2017)
Presentation / Conference Contribution
Dzogang, F., Goulding, J., Lightman, S., & Cristianini, N. (2017). Seasonal Variation in Collective Mood via Twitter Content and Medical Purchases. In Advances in Intelligent Data Analysis XVI (63-74). https://doi.org/10.1007/978-3-319-68765-0_6

The analysis of sentiment contained in vast amounts of Twitter messages has reliably shown seasonal patterns of variation in multiple studies, a finding that can have great importance in the understanding of seasonal affective disorders, particularly... Read More about Seasonal Variation in Collective Mood via Twitter Content and Medical Purchases.

Exploring the capabilities of Projection Augmented Relief Models (PARM) (2017)
Presentation / Conference Contribution
Priestnall, G., Goulding, J., Smith, A., & Arss, N. (2017). Exploring the capabilities of Projection Augmented Relief Models (PARM).

This paper explores the broad capabilities of physical landscape models when augmented by projection, termed Projection Augmented Relief Models (PARM). This includes experiences of developing PARM displays in public settings such as museums and visit... Read More about Exploring the capabilities of Projection Augmented Relief Models (PARM).

Event series prediction via non-homogeneous Poisson process modelling (2016)
Presentation / Conference Contribution
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.

Cross-system Recommendation: User-modelling via Social Media versus Self-Declared Preferences (2016)
Presentation / Conference Contribution
Alanazi, S., Goulding, J., & McAuley, D. (2016). Cross-system Recommendation: User-modelling via Social Media versus Self-Declared Preferences. In HT '16: Proceedings of the 27th ACM Conference on Hypertext and Social Media (183-188). https://doi.org/10.1145/2914586.2914640

© 2016 ACM. It is increasingly rare to encounter aWeb service that doesn't engage in some form of automated recommendation, with Collaborative Filtering (CF) techniques being virtually ubiquitous as the means for delivering relevant content. Yet seve... Read More about Cross-system Recommendation: User-modelling via Social Media versus Self-Declared Preferences.

A novel symbolization technique for time-series outlier detection (2015)
Presentation / Conference Contribution
Smith, G., & Goulding, J. (2015). A novel symbolization technique for time-series outlier detection. In 2015 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/BigData.2015.7364037

The detection of outliers in time series data is a core component of many data-mining applications and broadly applied in industrial applications. In large data sets algorithms that are efficient in both time and space are required. One area where sp... Read More about A novel symbolization technique for time-series outlier detection.

Towards computation of novel ideas from corpora of scientific text (2015)
Journal Article

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. (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.

A refined limit on the predictability of human mobility (2014)
Presentation / Conference Contribution
Smith, G., Wieser, R., Goulding, J., & Barrack, D. (2014). A refined limit on the predictability of human mobility. In 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom). https://doi.org/10.1109/PerCom.2014.6813948

It has been recently claimed that human movement is highly predictable. While an upper bound of 93% predictability was shown, this was based upon human movement trajectories of very high spatiotemporal granularity. Recent studies reduced this spatiot... Read More about A refined limit on the predictability of human mobility.

A data driven approach to mapping urban neighbourhoods (2014)
Presentation / Conference Contribution
Brindley, P., Goulding, J., & Wilson, M. L. (2014). A data driven approach to mapping urban neighbourhoods.

Neighbourhoods have been described by the UK Secretary of State for Communities and Local Government as the “building blocks of public service society”. Despite this, difficulties in data collection combined with the concept’s subjective nature have... Read More about A data driven approach to mapping urban neighbourhoods.

Towards optimal symbolization for time series comparisons (2013)
Presentation / Conference Contribution
Smith, G., Goulding, J., & Barrack, D. (2013). Towards optimal symbolization for time series comparisons. In 2013 IEEE 13th International Conference on Data Mining Workshops. https://doi.org/10.1109/ICDMW.2013.59

The abundance and value of mining large time series data sets has long been acknowledged. Ubiquitous in fields ranging from astronomy, biology and web science the size and number of these datasets continues to increase, a situation exacerbated by the... Read More about Towards optimal symbolization for time series comparisons.