NDLI: Time Series Modeling and Prediction Using Postfix Genetic Programming

Content Provider	IEEE Xplore Digital Library
Author	Dabhi, V.K. Chaudhary, S.
Copyright Year	2014
Description	Author affiliation: Inf. Technol. Dept., Dharmsinh Desai Univ., Nadiad, India (Dabhi, V.K.) \|\| IICT, Ahmedabad Univ., Ahmedabad, India (Chaudhary, S.)
Abstract	Traditional techniques for time series modeling can capture linear behavior 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 modeling 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 modeling 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 modeling 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 modeling and prediction problems.
Sponsorship	IEEE Harbin Sect.
Starting Page	307
Ending Page	314
File Size	569691
Page Count	8
File Format	PDF
ISBN	9781479949106
DOI	10.1109/ACCT.2014.33
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-02-08
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time series analysis Predictive models Mathematical model Sociology Semantics Data models Multi-step ahead prediction Time Series Modeling Postfix Genetic Programming One-step ahead prediction
Content Type	Text
Resource Type	Article

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