Earticle

초록

영어

It has been deemed as an effective tool of forecasting performance improvement to combine different component forecasting models. However, current nonlinear combining models are not able to meet the requirement of high forecasting accuracy in practice. To tackle this challenge, this paper constructs a hybrid, named genetic programming and least squared estimation based nonlinear combining method (GPLSE-NC), of a standard genetic programming (GP) algorithm and the least square estimation (LSE) method, based on which a new nonlinear combined forecasting model is proposed. To verify the feasibility of the proposed model, based on the container throughput data of Shanghai Port from January 2004 to November 2015, 4 different forecasting models are constructed and compared with the proposed GPLSE-NC combining model in terms of three forecasting performance evaluation criteria. The empirical results show significant superiority of the GPLSE-NC model over its rivals, which reveals that the proposed model has a great potential to be a powerful nonlinearly combine forecasting approach.

목차

Abstract
 1. Introduction
 2. Literature Review
 3. GPLSE-NC
  3.1. Genetic Programming Based On Least Square Estimation (GPLSE)
  3.2. The Framework of GPLSE-NC
 4. Component Models and Benchmark Combined Models
  4.1. Component Models
  4.2. Benchmark Combined Models
 5. Empirical Study
  5.1. Data Description and Evaluation Criteria
  5.2. Empirical Results
 References

키워드

저자

• Yingxiao Zhou [ School of Traffic and Transportation, Beijing Jiaotong University, China ]
• Peng Zhao [ School of Traffic and Transportation, Beijing Jiaotong University, China ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605