Home > Research > Researchers > Devon Barrow

Dr Devon Barrow

Formerly at Lancaster University

Current Research

My research investigates the application of active model combination methods such as Boosting and Bagging to time series data. It seeks to narrow the gap between the application of combination methods developed in machine learning and the use of these techniques in the forecasting of time series data which is traditionally approached through the use of statistical models. Some expected contributions are:

  1. Empirical evidence of the accuracy of active model combination (boosting and bagging) methods, relative to Ex-post model selection or combination for time series data.
  2. Evaluation of the relevance and impact of Boosting’s meta parameters for time series forecast performance. These parameters include choice and type of loss function, determination of model combination size, and the model combination method. Develop guidelines on the choice of such parameters under specific time series data conditions.
  3. Develop an analysis and theory about the behaviour of boosting for time series data in the presence of noise and outliers, where none exists.
  4. Development of new noise and outlier robust boosting algorithms for forecasting time series data.

Research Interests

My research interests include but are not limited to:

  • Neural network applications in forecasting and data mining
  • Time series forecasting (statistical and computational methods)
  • Model combination and selection (Forecasting and Data Mining)
  • Applications of meta-learning techniques to time series forecasting

Thesis Title

Active model combination - an evaluation and extension of Bagging and Boosting for time series forecasting

Thesis Outline

Since the seminal work by Bates and Granger (1969), the practice of combining two or more models, rather than selecting the single best, has consistently been shown to lead to improvements in accuracy. In forecasting, model combination aims to find an optimal weighting given a set of precalculated forecasts. In contrast, machine learning includes methods which simultaneously optimise individual models and the weights used to combine them. Bagging and boosting combine the results of complementary and diverse models generated by actively perturbing, reweighting and resampling training data. Despite large gains in predictive accuracy in classification, limited research assesses their efficacy on time series data. This thesis provides a critical review of the combination literature, and is the first literature survey of boosting for time series forecasting. The lack of rigorous empirical evidence on forecast accuracy of Bagging and boosting is identified as a major gap. To address this, a rigorous evaluation of Bagging and boosting adhering to recommendations of the forecasting literature is performed using robust error measures on a large set of real time series, exhibiting a representative set of features and dataset properties. Additionally there is a narrow focus on marginal extensions of boosting, and limited evidence of any gains in accuracy. A novel framework is proposed to explore the impact of varying boosting meta-parameters, and to evaluate the empirical accuracy of the resulting 96 boosting variants. The choice of base model and combination size are found to have the largest impact on forecast accuracy. Findings show that boosting overfits to noisy data, however no existing study investigates this crucial issue. New noise robust boosting methods are developed and evaluated for time series forecast models.


PhD in Management Science

Department of Management Science, Lancaster University Management School, Lancaster University, UK

Model selection and combination for time series forecasting with artificial neural networks

Dr. Sven F. Crone, Professor Robert Fildes


MSc (Honors) Computer Science

Department of Computer Science, University of Canterbury, Christchurch, New Zealand


BSc (Honors) Computer Science and Mathematics

Department of Computing and Information Technology, Department of Mathematics and Statistics, University of the West Indies, St. Augustine Campus, Trinidad and Tobago

ACCA Chartered Accountant (Affiliate)

ACCA, London, United Kingdom

Web Links


Career Details

Academic Work Experience

01/2013 - present

Main activities and responsibilities

03/2010 – 10/2011

Main activities and responsibilities


03/2008 – 07/2009

Main activities and responsibilities

01/2007 – 03/2008

Main activities and responsibilities


Post Doctoral Researcher
Lancaster University Management School

Forecasting and statistics postdoctoral research including applied academic research with companies in the field of forecasting, supply chain, marketing analytics and data mining.


Part-time Research Assistant
Department of Management Science, Lancaster University Management School

Applied academic research with companies such as:

1) Retail Express
2) Beiersdorf
3) Dong Energy

Lecturer of Mathematics, Finance and Computer Science
Monroe College, St. Lucia Campus, Barnard Hill, St. Lucia

Teaching undergraduate courses in database management excel spreadsheet modelling, management science and finance.

Research Assistant
Intelligent Computer Tutoring Group, University of Canterbury, New Zealand

Programming and project management support for research projects within the group specializing in artificial intelligence in education, including the design and implementation of intelligent tutoring system.

 Industry Work Experience


03/2008 – 07/2009

Main activities and responsibilities




07/2004 – 01/2006

Main activities and responsibilities



10/1999 – 08/2001

Main activities and responsibilities



Staff accountant II
PricewaterhouseCoopers (PwC) St. Lucia, Pointe Seraphine, P.O. Box 195, Castries, Saint Lucia 

  1. Lead in the planning and execution of audit serving as a Team Manager for small, medium and large companies across several industries including finance and banking, tourism, manufacturing and agriculture.

  2. Preparation of financial statements in accordance with IFRSs including performing preliminary and final analytical procedures over companies’ fiscal performance.

  3. Coaching and supervision of team members.

Staff Accountant 1
PricewaterhouseCoopers (PwC) St. Lucia, Pointe Seraphine, P.O. Box 195, Castries, Saint Lucia 

  1. Team member and junior supervisor for small, medium and large companies across several industries including finance and banking, tourism, manufacturing and agriculture.

  2. Preparation of financial statements in accordance with IFRSs, performing preliminary and final analysis procedures over companies’ fiscal performance.


Project Administrator
United Nations Framework Convention on Climate Change (UNFCC) – Greenhouse Gases Project, Ministry of Planning and Environment, Graham Louisy Administrative Building, Castries, Saint Lucia

Planning, Implementation and Coordination of Project activities including completion of the National Greenhouse Gases Inventory and the National Climate Change Strategy and Adaptation Plan.


View all (15) »