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Fuzzy Time-Series Models Based on Intuitionistic Fuzzy, Rough Set Fuzzy, and Differential Evolution

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Recent Innovations in Computing

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 855))

Abstract

Time-series forecasting is a vital concern for any data having temporal variations. Comparing with the other conventional time-series methodologies, the fuzzy time-series (FTS) proved its superiority. Substantial research using time-series forecasting to predict the stock index data has been found in the earlier works. The fuzzy sets approach alone cannot explain the data thoroughly. In this article, we have proposed three different methods of time-series forecasting. The first method is based on a rough set of FTS, a rule induction-based method; the second method is based on intuitionistic FTS. The last method is the extension of the second method using differential evolution. In the first model, a fuzzy algorithm based on rules is used to derive prediction rules from the time-series data and adopt an adaptive expectation model that replaces the fuzzy logical relationships or groups. In the second method, to split the universe of discourse into a non-uniform interval, a clustering algorithm-based intuitionistic fuzzy approach is used, taking care of the membership and non-membership function. Finally, the last method has been tuned for a better outcome using differential evolution. To examine the results, contrast analyses on the Taiwan stock exchange data and daily cases of COVID-19 pandemic prediction have been carried out. The outcome of the proposed approaches validates that the first and second techniques, showing promising results. However, the third method outperforms the other methods and the present techniques concerning the root-mean-square error metric.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Agartala, Tripura, 799046, India

    Partha Pratim Deb & Diptendu Bhattacharya

  2. Department of Computer Engineering, Tongmyong University, Busan, 48520, South Korea

    Indranath Chatterjee

Authors
  1. Partha Pratim Deb
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  2. Diptendu Bhattacharya
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  3. Indranath Chatterjee
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Editor information

Editors and Affiliations

  1. KIET Group of Institutions, Ghaziabad, India

    Pradeep Kumar Singh

  2. Department of CSE, Central University of Jammu, Jammu and Kashmir, India

    Yashwant Singh

  3. Department of Computer Engineering, NIT Kurukshetra, Kurukshetra, India

    Jitender Kumar Chhabra

  4. Faculty of Informatics, Eötvös Loránd University (ELTE), Budapest, Hungary

    Zoltán Illés

  5. Faculty of Informatics, Eötvös Loránd University (ELTE), Budapest, Hungary

    Chaman Verma

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Deb, P.P., Bhattacharya, D., Chatterjee, I. (2022). Fuzzy Time-Series Models Based on Intuitionistic Fuzzy, Rough Set Fuzzy, and Differential Evolution. In: Singh, P.K., Singh, Y., Chhabra, J.K., Illés, Z., Verma, C. (eds) Recent Innovations in Computing. Lecture Notes in Electrical Engineering, vol 855. Springer, Singapore. https://doi.org/10.1007/978-981-16-8892-8_10

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