Time Series Modeling and Prediction Using Postfix Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Dabhi:2014:ACCT,

• author = "Vipul K. Dabhi and Sanjay Chaudhary",
• booktitle = "Fourth International Conference on Advanced Computing Communication Technologies (ACCT 2014)",
• title = "Time Series Modeling and Prediction Using Postfix Genetic Programming",
• year = "2014",
• month = feb,
• pages = "307--314",
• keywords = "genetic algorithms, genetic programming, series Modelling, Postfix Genetic Programming, One-step ahead prediction, Multi-step ahead prediction",
• DOI = "doi:10.1109/ACCT.2014.33",
• size = "8 pages",
• abstract = "Traditional techniques for time series modelling can capture linear behaviour of data and lack the ability to identify nonlinear patterns in time series. Therefore, machine learning techniques like Neural Network or Genetic Programming (GP) are used by practitioners for modelling nonlinear and irregular time series. GP is preferred over other techniques because it does not presume model structure a priori. This paper introduces the use of Postfix-GP, a postfix notation based GP, for real-world nonlinear time series modelling problems. The Postfix-GP uses linear genome representation and stack based evaluation to reduce space-time complexity of GP. The Postfix-GP is applied on two real time series modelling problems: sunspots and river flow series. Performance of evolved Postfix-GP models on training data and out-of-sample data are compared with those obtained by others using EGIPSYS. The obtained results indicate that Postfix-GP offers a new possibility for solving time series modelling and prediction problems.",
• notes = "Also known as \cite{6783469}",

}

Genetic Programming entries for Vipul K Dabhi Sanjay Chaudhary

Citations