Temperature prediction and TAIFEX forecasting based on fuzzy relationships and MTPSO techniques

  • Authors:

  • Ling-Yuan Hsu;Shi-Jinn Horng;Tzong-Wann Kao;Yuan-Hsin Chen;Ray-Shine Run;Rong-Jian Chen;Jui-Lin Lai;I-Hong Kuo

  • Affiliations:

  • Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, 106 Taipei, Taiwan and Department of Information Management, ST. Mary's Medicine N ...;Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, 106 Taipei, Taiwan and Department of Electrical Engineering, National Taiwan Unive ...;Department of Electronic Engineering, Technology and Science Institute of Northern Taiwan, Taipei, Taiwan;Department of Electronic Engineering, National United University, 36003 Miao-Li, Taiwan;Department of Electronic Engineering, National United University, 36003 Miao-Li, Taiwan;Department of Electronic Engineering, National United University, 36003 Miao-Li, Taiwan;Department of Electronic Engineering, National United University, 36003 Miao-Li, Taiwan;Department of Information Management, ST. Mary's Medicine Nursing and Management College, I-Lan, Taiwan

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2010

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Abstract

In this paper, we proposed a modified turbulent particle swarm optimization (named MTPSO) method for the temperature prediction and the Taiwan Futures Exchange (TAIFEX) forecasting, based on the two-factor fuzzy time series and particle swarm optimization. The MTPSO model can be dealt with two main factors easily and accurately, which are the lengths of intervals and the content of forecast rules. The experimental results of the temperature prediction and the TAIFEX forecasting show that the proposed model is better than any existing models and it can get better quality solutions based on the high-order fuzzy time series, respectively.