MPhil-PhD transfer presentation
Room: A109 (College building)
Title: Motif Memory Nets
Abstract: Motifs are recurring patterns in time-series data that have the power to explain or forecast future events/data points. In the context of lifelong learning of a time-continuous dataset, “motif awareness” would improve modelling accuracy when compared to “motif naïve” generalizations. This study introduces a “motif aware” methodology called Motif Memory Nets (MMN), an ensemble approach based on a multi-column architecture of deep feedforward neural networks, employing a memory framework using Dynamic Time Warping. MMN identify, remember and recall “models” of repeating motifs in time continuous, multivariate time-series data, achieving an improvement in accuracy and, in some cases, parsimony over generalised approaches tested on the same problems. The contributions of this work are several: Firstly, this study introduces novel memory augmentation using a simple memory crystallization methodology that does not require traditional “gating”. Secondly, for memory retrieval, it is found that it is not necessary to fully learn the complex and potentially sparse distribution of the way a “memory associates with the problem space”, instead using a conditional framework drawn from the idea of “motif discovery” (or “similarity”) results in a much reduced memory retrieval and balancing task. Thirdly, MMN’s deep architecture draws on several threads in the literature applying “memory augmentation” but uses a synchronous, parallel architecture which can support distilling, while allowing MMNs to be theoretically deployable across processes and machines. Fourthly, MMNs are successfully applied to a complex, real-world dataset in the domain of Finance, a novel application for memory augmented approaches.
Keywords: Deep neural network, memory model, motif, dynamic time warping, multi-column, ensemble, time-series, lifelong learning.
Short bio: Dan Philps is a career Fund Manager and quantitative finance researcher and is working towards achieving a PhD in Machine Learning, applied (currently) to the domain of Finance, with supervisors Prof Artur Garcez and Dr Tillman Weyde. Commercially, Mr. Philps is Head of Rothko Investment Strategies – the quantitative equity investment group – and chairs the Rothko Investment and Research Committee. Prior to this, he was a Senior Fund Manager in Mondrian Investment Partners’ Global Fixed Income and Currencies team and before joining Mondrian, in 1998, Mr. Philps was a Consultant Quantitative Analyst/Programmer in the equity and derivatives businesses of Dresdner-KB, Bankers Trust and Barclays Capital, specializing in trading and risk models. Mr. Philps has a BSc (Hons) from King’s College London, is a CFA Charterholder, a member of the CFA Institute and a member of the CFA Society of the UK.