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

Jae-Seong Hwang, Sang-Soo Lee (), Jeong-Won Gil and Choul-Ki Lee
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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

Sustainability, 2024, vol. 16, issue 14, 1-11

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.

Keywords: batch size; time series data; deep learning; FFT; hyper-parameter (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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