Automatically Defined Templates for Improved Prediction of Non-stationary, Nonlinear Time Series in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@PhdThesis{Moskowitz:thesis,
  author =       "David Moskowitz",
  title =        "Automatically Defined Templates for Improved
                 Prediction of Non-stationary, Nonlinear Time Series in
                 Genetic Programming",
  school =       "College of Engineering and Computing, Nova
                 Southeastern University",
  year =         "2016",
  address =      "USA",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://nsuworks.nova.edu/gscis_etd/953/",
  URL =          "http://nsuworks.nova.edu/cgi/viewcontent.cgi?article=1954&context=gscis_etd",
  URL =          "http://gradworks.umi.com/10/09/10092209.html",
  size =         "272 pages",
  abstract =     "Soft methods of artificial intelligence are often used
                 in the prediction of non-deterministic time series that
                 cannot be modelled using standard econometric methods.
                 These series, such as occur in finance, often undergo
                 changes to their underlying data generation process
                 resulting in inaccurate approximations or requiring
                 additional human judgement and input in the process,
                 hindering the potential for automated
                 solutions.

                 Genetic programming (GP) is a class of nature-inspired
                 algorithms that aims to evolve a population of computer
                 programs to solve a target problem. GP has been applied
                 to time series prediction in finance and other domains.
                 However, most GP-based approaches to these prediction
                 problems do not consider regime change.

                 This paper introduces two new genetic programming
                 modularity techniques, collectively referred to as
                 automatically defined templates, which better enable
                 prediction of time series involving regime change.
                 These methods, based on earlier established GP
                 modularity techniques, take inspiration from software
                 design patterns and are more closely modeled after the
                 way humans actually develop software. Specifically, a
                 regime detection branch is incorporated into the GP
                 paradigm. Regime specific behaviour evolves in a
                 separate program branch, implementing the template
                 method pattern.

                 A system was developed to test, validate, and compare
                 the proposed approach with earlier approaches to GP
                 modularity. Prediction experiments were performed on
                 synthetic time series and on the S&P 500 index. The
                 performance of the proposed approach was evaluated by
                 comparing prediction accuracy with existing
                 methods.

                 One of the two techniques proposed is shown to
                 significantly improve performance of time series
                 prediction in series undergoing regime change. The
                 second proposed technique did not show any improvement
                 and performed generally worse than existing methods or
                 the canonical approaches. The difference in relative
                 performance was shown to be due to a decoupling of
                 reusable modules from the evolving main program
                 population. This observation also explains earlier
                 results regarding the inferior performance of genetic
                 programming techniques using a similar, decoupled
                 approach. Applied to financial time series prediction,
                 the proposed approach beat a buy and hold return on the
                 S&P 500 index as well as the return achieved by other
                 regime aware genetic programming methodologies. No
                 approach tested beat the benchmark return when
                 factoring in transaction costs.",
  notes =        "Time Series Prediction and the Stock Market, Patterns
                 in Software Engineering

                 Supervisor Sumitra Mukherjee",
}

Genetic Programming entries for David Moskowitz

Citations