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FSVM: Time Series Forecasting Using Fuzzy Support Vector Machine

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Intelligent Data Engineering and Analytics (FICTA 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 371))

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Abstract

Supervised machine learning approaches have become increasingly popular in time series forecasting. In many types of ANN models, feed-forward multi-layer perceptron’s have been utilized to explain fuzzy logical relationships (FLRs). Due to the complex network architecture and the problem with the choice of ad-hoc architecture, the performance is still vulnerable. The majority of the literature either selects the highest membership value, fuzzy set rank, or fuzzy set subscript for determining the FLRs. Even though the authors only considered a subset of the membership values that are associated with each fuzzy set, this causes a loss of information or knowledge and reduces the model’s capacity for making predictions. Motivated by the aforementioned, we have taken into account combining the membership values of each fuzzy set with its equivalent crisp data in order to represent the FLRs. The present paper proposes a fuzzy support vector machine (FSVM) training method as a result of this. The effectiveness of the suggested FSVM models is demonstrated using various time series datasets. A few existing models with RMSE and SMAPE characteristics are used to test the model’s efficacy.

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Acknowledgements

We would like to thank Chaitanya group of institutions Chairman Sri K. V. V. Satyanarayana Raju, Vice Chairman K. Sasi Kiran Varma for providing the necessary infrastructure. Dr. N. Leelavathy for her invaluable suggestions which led to improving the quality of this paper.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Godavari Institute of Engineering and Technology (Autonomous), Rajahmahendravaram, India

    N. Sravani, N. Sravani, B. Sujatha, R. Tamilkodi & N. Leelavathy

Authors
  1. N. Sravani
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  2. B. Sujatha
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  3. R. Tamilkodi
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  4. N. Leelavathy
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Corresponding author

Correspondence to N. Sravani .

Editor information

Editors and Affiliations

  1. Department of Electronics Engineering, Faculty of Engineering and Technology (UNSIET), Veer Bahadur Singh Purvanchal University, Jaunpur, Uttar Pradesh, India

    Vikrant Bhateja

  2. Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Fiona Carroll

  3. Faculty of Engineering, University of Porto, Porto, Portugal

    João Manuel R. S. Tavares

  4. Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Sandeep Singh Sengar

  5. Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

    Peter Peer

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Sravani, N., Sujatha, B., Tamilkodi, R., Leelavathy, N. (2023). FSVM: Time Series Forecasting Using Fuzzy Support Vector Machine. In: Bhateja, V., Carroll, F., Tavares, J.M.R.S., Sengar, S.S., Peer, P. (eds) Intelligent Data Engineering and Analytics. FICTA 2023. Smart Innovation, Systems and Technologies, vol 371. Springer, Singapore. https://doi.org/10.1007/978-981-99-6706-3_25

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