@article{ART002927605},
author={Tae-Jin Park and Gab-Sig Sim},
title={A Comparative study on smoothing techniques for performance improvement of LSTM learning model},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2023},
volume={28},
number={1},
pages={17-26},
doi={10.9708/jksci.2023.28.01.017}
TY - JOUR
AU - Tae-Jin Park
AU - Gab-Sig Sim
TI - A Comparative study on smoothing techniques for performance improvement of LSTM learning model
JO - Journal of The Korea Society of Computer and Information
PY - 2023
VL - 28
IS - 1
PB - The Korean Society Of Computer And Information
SP - 17
EP - 26
SN - 1598-849X
AB - In this paper, we propose a several smoothing techniques are compared and applied to increase the application of the LSTM-based learning model and its effectiveness. The applied smoothing technique is Savitky-Golay, exponential smoothing, and weighted moving average. Through this study, the LSTM algorithm with the Savitky-Golay filter applied in the preprocessing process showed significant best results in prediction performance than the result value shown when applying the LSTM model to Bitcoin data. To confirm the predictive performance results, the learning loss rate and verification loss rate according to the Savitzky-Golay LSTM model were compared with the case of LSTM used to remove complex factors from Bitcoin price prediction, and experimented with an average value of 20 times to increase its reliability. As a result, values of (3.0556, 0.00005) and (1.4659, 0.00002) could be obtained. As a result, since crypto-currencies such as Bitcoin have more volatility than stocks, noise was removed by applying the Savitzky-Golay in the data preprocessing process, and the data after preprocessing were obtained the most-significant to increase the Bitcoin prediction rate through LSTM neural network learning.
KW - LSTM/GRU learning model;Filters:Savitky-Golay/single exponential smoothing/weighted moving average;time series data;pre-processing
DO - 10.9708/jksci.2023.28.01.017
ER -
Tae-Jin Park and Gab-Sig Sim. (2023). A Comparative study on smoothing techniques for performance improvement of LSTM learning model. Journal of The Korea Society of Computer and Information, 28(1), 17-26.
Tae-Jin Park and Gab-Sig Sim. 2023, "A Comparative study on smoothing techniques for performance improvement of LSTM learning model", Journal of The Korea Society of Computer and Information, vol.28, no.1 pp.17-26. Available from: doi:10.9708/jksci.2023.28.01.017
Tae-Jin Park, Gab-Sig Sim "A Comparative study on smoothing techniques for performance improvement of LSTM learning model" Journal of The Korea Society of Computer and Information 28.1 pp.17-26 (2023) : 17.
Tae-Jin Park, Gab-Sig Sim. A Comparative study on smoothing techniques for performance improvement of LSTM learning model. 2023; 28(1), 17-26. Available from: doi:10.9708/jksci.2023.28.01.017
Tae-Jin Park and Gab-Sig Sim. "A Comparative study on smoothing techniques for performance improvement of LSTM learning model" Journal of The Korea Society of Computer and Information 28, no.1 (2023) : 17-26.doi: 10.9708/jksci.2023.28.01.017
Tae-Jin Park; Gab-Sig Sim. A Comparative study on smoothing techniques for performance improvement of LSTM learning model. Journal of The Korea Society of Computer and Information, 28(1), 17-26. doi: 10.9708/jksci.2023.28.01.017
Tae-Jin Park; Gab-Sig Sim. A Comparative study on smoothing techniques for performance improvement of LSTM learning model. Journal of The Korea Society of Computer and Information. 2023; 28(1) 17-26. doi: 10.9708/jksci.2023.28.01.017
Tae-Jin Park, Gab-Sig Sim. A Comparative study on smoothing techniques for performance improvement of LSTM learning model. 2023; 28(1), 17-26. Available from: doi:10.9708/jksci.2023.28.01.017
Tae-Jin Park and Gab-Sig Sim. "A Comparative study on smoothing techniques for performance improvement of LSTM learning model" Journal of The Korea Society of Computer and Information 28, no.1 (2023) : 17-26.doi: 10.9708/jksci.2023.28.01.017
