- author = "M. A. Kaboudan",
- title = "Forecasting with computer-evolved model specifications: a genetic programming application",
- journal = "Computers and Operations Research",
- year = "2003",
- volume = "30",
- number = "11",
- pages = "1661--1681",
- month = sep,
- email = "mahmoud_kaboudan@Redlands.edu",
- keywords = "genetic algorithms, genetic programming, Computational methods, Nonlinear dynamic systems, Time series, Sunspot numbers",
-
URL = "
http://www.sciencedirect.com/science/article/B6VC5-47P1N3H-1/2/d89d466d6ed20bb2d2da43b3701f351b",
- ISSN = "0305-0548",
-
DOI = "
doi:10.1016/S0305-0548(02)00098-9",
-
abstract = "This paper uses genetic programming (GP) to evolve
model specifications of time series data. GP is a
computerized random search optimisation algorithm that
assembles equations until it identifies the fittest
one. The technique is applied here to artificially
simulated data first then to real-world sunspot
numbers. One-step-ahead forecasts produced by the
fittest of computer-evolved models are evaluated and
compared with alternatives. The results suggest that GP
may produce reasonable forecasts if their user selects
appropriate input variables and comprehends the process
investigated. Further, the technique appears promising
in forecasting noisy complex series perhaps better than
other existing methods. It is suitable for decision
makers who set high priority on obtaining accurate
forecasts rather than on probing into and approximating
the underlying data generating process.
This paper contains a brief introduction and an evaluation of the use of genetic programming (GP) in forecasting time series. GP is a computerized random search optimization technique based upon Darwin's theory of evolution. The algorithm is first applied to model and forecast artificially simulated linear and nonlinear time series. Results are used to evaluate the effectiveness of GP as a forecasting technique. It is then applied to model and forecast sunspot numbers--the most frequently analyzed and forecasted series. An autoregressive and a threshold nonlinear dynamical systems to capture the dynamics of the irregular sunspot numbers' cycle were tested using GP. The latter delivered estimated equations yielding the lowest mean square error ever reported for the series. This paper demonstrates that GP's forecasting capabilities depend on the structure and complexity of the process to model. Skills and intuition of GP's user are its limitation.",
