Social disruption events can be modelled by time series and are influenced by other event series (local, neighbour cities and the major cities), posts in social media, news, and economic circumstance, etc. The extracted information from the media forms features (Ramakrishnan et al 2014). Each feature is represented as a time series, and the data is a large set of time series. The aim of the project is to select a subset of time series that are informative for the prediction the future civil unrest events. Some feature selection work of time series can be found in (Kim 2012; Sun et al 2014).


  • First class Honours (or Master by research) in Computer Science or Statistics.
  • Proficiency in programming (any language).
  • IELTS at least 6.5 overall not lower than 6.0 in each component for an overseas applicant.

Key Contacts

Professor Jiuyong Li and Jixue Liu
School of Information Technology and Mathematical Sciences, University of South Australia



Ramakrishnan, N et al (2014). ‘Beating the news’ with EMBERS: forecasting civil unrest using open source indicators. KDD 2014: 1799-1808.
Kim, M (2012). Time-series dimensionality reduction via Granger causality. IEEE Signal Processing Letters,19(10), 611-614.
Sun, Y, Li, J, Liu, J, Chow, C, Sun, B, Wang, R (2014). Using causal discovery for feature selection in multivariate numerical time series, Machine Learning, advance access.