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http://dx.doi.org/10.36498/kbigdt.2022.7.2.217

Comparative Analysis of Prediction Performance of Aperiodic Time Series Data using LSTM and Bi-LSTM  

Ju-Hyung Lee (배재대학교 모바일소프트웨어학과)
Jun-Ki Hong (배재대학교 컴퓨터공학과)
Publication Information
The Journal of Bigdata / v.7, no.2, 2022 , pp. 217-224 More about this Journal
Abstract
Since online shopping has become common, people can easily buy fashion goods anytime, anywhere. Therefore, consumers quickly respond to various environmental variables such as weather and sales prices. Therefore, utilizing big data for efficient inventory management has become very important in the fashion industry. In this paper, the changes in sales volume of fashion goods due to changes in temperature is analyzed via the proposed big data analysis algorithm by utilizing actual big data from Korean fashion company 'A'. According to the simulation results, it was confirmed that Bidirectional-LSTM(Bi-LSTM) compared to LSTM(Long Short-Term Memory) takes more simulation time about more than 50%, but the prediction accuracy of non-periodic time series data such as clothing product sales data is the same.
Keywords
LSTM; Bidirectional-LSTM; aperiodic time series data; prediction;
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Times Cited By KSCI : 1  (Citation Analysis)
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