EPSRC Centre for Doctoral Training Cloud Computing for Big Data


Lauren Roberts

PhD title

Real-time monitoring and forecasting of time series

The modelling of time series data can help us make real-time decisions. Often, data is available on more than one variable. Joint models can be fitted to provide more informed inferences. Advances in the Internet of Things have led to large amounts of smart-sensors. These produce high volumes of time series data.

Among these devices are continuous glucose monitoring (CGM) devices. CGM are sensors placed under the skin that regularly report blood glucose levels.

Type 2 diabetes is a disease which causes blood glucose levels to become dangerously high (hyperglycaemia). It is widely known that exercise directly affects blood glucose levels. It can help reverse the effects of type 2 diabetes.

Through joint modelling of patient blood glucose levels (from a CGM device) with patient activity levels (through collecting data from a wrist-worn accelerometer) we can forecast blood glucose levels. This allows the patient to act and to prevent the hyperglycaemic episode. It also gives us more accurate blood glucose forecasts.


Darren Wilkinson, Sarah Heaps, Paul Watson


Automating the placement of time series models for IoT healthcare applications - Roberts, L. Michalák, P. Heaps, S. Trenell, M. Wilkinson, D.J. Watson P - IEEE eScience conference in Amsterdam - 2018 

Presented Poster at International Society for Bayesian Analysis conference - International Society for Bayesian Analysis conference, Edinburgh, UK - 2018