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A Two-Fold Multi-objective Multi-verse Optimization-Based Time Series Forecasting

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Proceedings of the Seventh International Conference on Mathematics and Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1412))

Abstract

In this study, to overcome error due to high-dimensional data and to get the best forecasting prediction for time series data, we employ a feature selection method to obtain the best exploitation and exploratory performance. Due to a large number of irrelevant factors within data, it is imperative to classify the tasks by using a feature selection method. Therefore, a two-fold multi-objective multi-verse optimization as a feature selection optimization method has been proposed to obtain a trade-off between minimization loss and minimization of the number of features selected. The Convolution Neural Network (CNN) has been used as a basic predictor. A dynamic error correction is also proposed to reduce the error further to the deep learning models to get the best time series forecasting. However, many Multi-Objective Optimization techniques have been used to deal with high-dimensional data, the proposed method showed the best trade-off for feature selection.

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Author information

Authors and Affiliations

  1. Department of Ocean Engineering and Naval Architecture, IIT Kharagpur, Kharagpur, 721302, India

    Cherry Tandu

  2. Department of Mechanical Engineering, IIT Kharagpur, Kharagpur, 721302, India

    Mohit Kosuri

  3. Business School, University of Edinburgh, 29 Buccleuch Pl, Edinburgh, EH8 9JS, UK

    Sobhan Sarkar

  4. Department of Industrial and Systems Engineering, IIT Kharagpur, Kharagpur, 721302, India

    J. Maiti

Authors
  1. Cherry Tandu
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  2. Mohit Kosuri
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  3. Sobhan Sarkar
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  4. J. Maiti
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Corresponding author

Correspondence to Sobhan Sarkar .

Editor information

Editors and Affiliations

  1. Department of Information Technology, Maulana Abul Kalam Azad University of Technology, Haringhata, India

    Debasis Giri

  2. The University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  3. Department of Mathematics, Indian Institute of Technology Madras, Chennai, India

    Saminathan Ponnusamy

  4. Technical University of Denmark, Kongens Lyngby, Denmark

    Weizhi Meng

  5. Department of Computer Engineering, Ondokuz Mayis University, Atakum, Turkey

    Sedat Akleylek

  6. Department of Information Technology, Indian Institute of Engineering Science and Technology, West Bengal, India

    Santi Prasad Maity

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

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Tandu, C., Kosuri, M., Sarkar, S., Maiti, J. (2022). A Two-Fold Multi-objective Multi-verse Optimization-Based Time Series Forecasting. In: Giri, D., Raymond Choo, KK., Ponnusamy, S., Meng, W., Akleylek, S., Prasad Maity, S. (eds) Proceedings of the Seventh International Conference on Mathematics and Computing . Advances in Intelligent Systems and Computing, vol 1412. Springer, Singapore. https://doi.org/10.1007/978-981-16-6890-6_55

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