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A novel method for forecasting time series based on fuzzy logic and visibility graph

Author

Listed:
  • Rong Zhang

    (University of Electronic Science and Technology of China
    Southwest University)

  • Baabak Ashuri

    (Georgia Institute of Technology)

  • Yong Deng

    (University of Electronic Science and Technology of China
    Southwest University)

Abstract

Time series attracts much attention for its remarkable forecasting potential. This paper discusses how fuzzy logic improves accuracy when forecasting time series using visibility graph and presents a novel method to make more accurate predictions. In the proposed method, historical data is firstly converted into a visibility graph. Then, the strategy of link prediction is utilized to preliminarily forecast the future data. Eventually, the future data is revised based on fuzzy logic. To demonstrate the performance, the proposed method is applied to forecast Construction Cost Index, Taiwan Stock Index and student enrollments. The results show that fuzzy logic is able to improve the accuracy by designing appropriate fuzzy rules. In addition, through comparison, it is proved that our method has high flexibility and predictability. It is expected that our work will not only make contributions to the theoretical study of time series forecasting, but also be beneficial to practical areas such as economy and engineering by providing more accurate predictions.

Suggested Citation

  • Rong Zhang & Baabak Ashuri & Yong Deng, 2017. "A novel method for forecasting time series based on fuzzy logic and visibility graph," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(4), pages 759-783, December.
    • Handle: RePEc:spr:advdac:v:11:y:2017:i:4:d:10.1007_s11634-017-0300-3
    DOI: 10.1007/s11634-017-0300-3
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