Earticle

초록

영어

We compared empirically the forecast accuracies of the LSTM model, and the GRU model, because two models were commonly used in deep learning time series forecast. LSTM compensates for the vanishing gradient problem of RNN and provides good performance, but takes a long time to calculate. GRU was proposed as a model that simplifies the structure of LSTM to reduce computation time. Data used in the model is 163 days. We compared the forecast results for 33 days. We collected the stock closing prices of the top 4 companies by market capitalization in Korea such as “Samsung Electronics”, and “LG Energy”, “SK Hynix”, “Samsung Bio”. The collection period is from January 2, 2024, to August 30, 2024. The paired t-test is used to compare the accuracy of forecasts by the two methods because conditions are same. The null hypothesis that the accuracy of the two methods for the four stock closing prices were the same were not rejected at the significance level of 5% except Samsung Electronics. However, in four cases, the averages of the GRU errors were lower than those of the LSTM errors. And we can find that GRU shows similar performance while requiring less computation than LSTM. Graphs and boxplots confirmed the results of the hypothesis tests. Because in other empirical studies the results may vary, many additional empirical studies are needed.

목차

Abstract
1. Introduction
2. Forecast Accuracy Comparison of LSTM and GRU
2.1 Structure of LSTM and GRU
2.2 Comparison of LSTM and GRU
3. Conclusion
Acknowledgement
References

키워드

저자

• Gui Yeol Ryu [ Professor, Department of Software, Seokyeong University, Seoul 02713, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380