All Outputs (3)

A novel symbolization technique for time-series outlier detection (2015)
Conference Proceeding
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.

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.

Modelling of building interiors with mobile phone sensor data (2015)
Journal Article
Rosser, J., Morley, J., & Smith, G. (2015). Modelling of building interiors with mobile phone sensor data. ISPRS International Journal of Geo-Information, 4(2), https://doi.org/10.3390/ijgi4020989

Creating as-built plans of building interiors is a challenging task. In this paper we present a semi-automatic modelling system for creating residential building interior plans and their integration with existing map data to produce building models.... Read More about Modelling of building interiors with mobile phone sensor data.