Primary supervisorDavid Dowe
- Farshid Vahid
Research areaMachine Learning and Deep Learning
There are sometimes emerging prolonged periods of highly persistent evolution in time series. Share prices of successful start-ups, the number of confirmed cases in a pandemic (e.g., COVID-19 coronavirus), and the production of renewable energy in different countries are some examples. In almost all contexts, these episodes happen in several time series, but not necessarily at the same calendar time. In this project, we propose a method for identifying and classifying such emerging asynchronous trends. The goal is to be able to predict how a new emerging trend will develop using similar trends that started earlier in other time series.
Part of what will be done will be to identify sufficiently similar time series - and to pool (or combine) relevant data.
One of the approaches that will be used will the information-theoretic Bayesian minimum message length (MML) principle.
PhD, possibly Master’s (Minor Thesis) or Honours
Chen, Li and Gao, Jiti and Vahid, Farshid, Global Temperatures and Greenhouse Gases: A Common Features Approach (September 30, 2019). Available at SSRN: https://ssrn.com/abstract=3461418 or http://dx.doi.org/10.2139/ssrn.3461418
Fitzgibbon, L.J., D. L. Dowe and F. Vahid (2004). Minimum Message Length Autoregressive Model Order Selection. In M. Palanaswami, C. Chandra Sekhar, G. Kumar Venayagamoorthy, S. Mohan and M. K. Ghantasala (eds.), International Conference on Intelligent Sensing and Information Processing (ICISIP), Chennai, India, 4-7 January 2004 (ISBN: 0-7803-8243-9, IEEE Catalogue Number: 04EX783), pp439-444.
Molloy, S., D.W. Albrecht, D. L. Dowe and K.M. Ting (2006). Model-Based Clustering of Sequential Data, Proc. 5th Annual Hawaii Intl. Conf. on Statistics, Mathematics and Related Fields, 22 pages, 16th - 18th January, 2006, Hawaii, U.S.A.
P. J. Tan and D. L. Dowe (2003). MML Inference of Decision Graphs with Multi-Way Joins and Dynamic Attributes, Proc. 16th Australian Joint Conference on Artificial Intelligence (AI'03), Perth, Australia, 3-5 Dec. 2003, Published in Lecture Notes in Artificial Intelligence (LNAI) 2903, Springer-Verlag, pp269-281
Wallace, C.S. (2005), ``Statistical and Inductive Inference by Minimum Message Length'', Springer (Link to the preface [and p vi, also here])
Important is a knowledge of at least one of mathematics, statistics and/or machine learning principles - including at least interest in probability theory - with at least one of these at least to the level of an undergraduate degree. Candidates should also have a strong computer science background with good programming skills, particularly in at least one of Python, Java and/or MATLAB.