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Improving Time Series Prediction with Deep Belief Network

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Abstract

In this paper, the time series data prediction is done using Deep Belief Network (DBN). The time series data chosen are stock price data, exchange rate data, and electricity consumption data. DBN predicts these three datasets. Particle Swarm Optimization and Local Linear Wavelet Neural Network are also used for prediction of these three datasets. The Root Mean Square Error and Mean Absolute Percentage Error parameters are used to validate the performance of the algorithm. DBNs are more efficient than other machine learning algorithms because they generate less error. They are fault tolerant and use parallel processing. They avoid over fitting and increase the model generalization.

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Authors and Affiliations

  1. Government College of Engineering, Kalahandi, India

    Soumya Das

  2. Department of Electronics and Telecommunication Engineering, Indira Gandhi Institute of Technology, Sarang, India

    Monalisa Nayak

  3. Department of Information Technology, Veer Surendra Sai University of Technology, Burla, India

    Manas Ranjan Senapati

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  1. Soumya Das
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  2. Monalisa Nayak
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  3. Manas Ranjan Senapati
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Correspondence to Manas Ranjan Senapati.

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Das, S., Nayak, M. & Senapati, M.R. Improving Time Series Prediction with Deep Belief Network. J. Inst. Eng. India Ser. B 104, 1103–1118 (2023). https://doi.org/10.1007/s40031-023-00912-0

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  • DOI: https://doi.org/10.1007/s40031-023-00912-0

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