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Determination of Optimal Batch Size of Deep Learning Models with Time Series Data

Author

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  • Jae-Seong Hwang

    (Transportation Research Institute, Ajou University, Suwon 16499, Republic of Korea)

  • Sang-Soo Lee

    (Department of Transportation System Engineering, Ajou University, Suwon 16499, Republic of Korea)

  • Jeong-Won Gil

    (Department of DNA+ Convergence, Ajou University, Suwon 16499, Republic of Korea)

  • Choul-Ki Lee

    (Department of Transportation System Engineering, Ajou University, Suwon 16499, Republic of Korea)

Abstract

This paper presents a new method to determine the optimal batch size for applying deep learning models with time series data. A set of batch sizes is determined by considering the length of the repetition pattern of the data using the Fast Fourier Transform (FFT). A comparative analysis is conducted to identify the impact of varying batch sizes on prediction errors for the three deep learning models. The results show that the RNN model has the optimal batch size that produces the minimum prediction error. In the DNN and CNN models, the optimal batch size is not correlated with the repetition pattern of time series data. Therefore, it is not recommended to apply CNN and DNN models of time series data. However, if used, a small batch size can be selected to reduce training time. In addition, the range of prediction error according to batch size is significantly larger for RNN models compared to DNN and CNN models.

Suggested Citation

  • Jae-Seong Hwang & Sang-Soo Lee & Jeong-Won Gil & Choul-Ki Lee, 2024. "Determination of Optimal Batch Size of Deep Learning Models with Time Series Data," Sustainability, MDPI, vol. 16(14), pages 1-11, July.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:14:p:5936-:d:1433611
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    References listed on IDEAS

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    1. Yi Zhang & A. Mesaros & K. Fujita & S. D. Edkins & M. H. Hamidian & K. Ch’ng & H. Eisaki & S. Uchida & J. C. Séamus Davis & Ehsan Khatami & Eun-Ah Kim, 2019. "Machine learning in electronic-quantum-matter imaging experiments," Nature, Nature, vol. 570(7762), pages 484-490, June.
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