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Does deep learning over-fit - and, if so, how does it work on time series?

Primary supervisor

David Dowe


  • M Shelton Peiris

Theory and applications in data analytics of time series became popular in the past few years due to the availability of data in various sources. This project aims to investigate and generalise Hybrid and Neural Network methods in time series to develop forecast algorithms. The methodology will be developed as a theoretical construct together with wide variety of applications.

Student cohort

Double Semester


  D. L. Dowe (2008a), "Foreword re C. S. Wallace", Computer Journal, Vol. 51, No. 5 (Sept. 2008) [Christopher Stewart WALLACE (1933-2004) memorial special issue [and front cover and back cover]], pp523-560 (and here). 10.1093/comjnl/bxm117

  D. L. Dowe (2011a), "MML, hybrid Bayesian network graphical models, statistical consistency, invariance and uniqueness", Handbook of the Philosophy of Science - (HPS Volume 7) Philosophy of Statistics, P.S. Bandyopadhyay and M.R. Forster (eds.), Elsevier, [ISBN: 978-0-444-51862-0 {ISBN 10: 0-444-51542-9 / ISBN 13: 978-0-444-51862-0}], pp901-982, 1/June/2011

  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.

  Wallace, C.S. (2005), ``Statistical and Inductive Inference by Minimum Message Length'', Springer  (Link to the preface [and p vi, also here])

  Wallace, C.S. and D.M. Boulton (1968), ``An information measure for classification'', Computer Journal, Vol 11, No 2, August 1968, pp 185-194

  Wallace, C.S. and D.L. Dowe (1999a). Minimum Message Length and Kolmogorov Complexity, Computer Journal (special issue on Kolmogorov complexity), Vol. 42, No. 4, pp270-283

  Wallace, C.S. and P.R. Freeman (1987).  Estimation and inference by compact coding. J. Royal Statist. Soc. B, 49, 240–252.

Required knowledge

At least first year undergraduate mathematics, preferably more.

Statistics, machine learning and/or data science at least to the level of an undergraduate degree.

An ability to program.