• The Journal of Economics, Marketing and Management
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• v.9 no.2
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• pp.1-10
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• 2021

Purpose: In this work, we examine several time series models to predict internet and mobile transaction amount in South Korea, whereas Jeong (2020) has obtained the optimal forecasts for online shopping transaction amount by using time series models. Additionally, optimal forecasts based on the model considered can be calculated and applied to the Corona 19 situation. Research design, data, and methodology: The data are extracted from the online shopping trend survey of the National Statistical Office, and homogeneous and comparable in size based on 46 realizations sampled from January 2007 to October 2020. To achieve the goal of this work, both multiplicative ARIMA model and Holt-Winters Multiplicative seasonality method are taken into account. In addition, goodness-of-fit measures are used as crucial tools of the appropriate construction of forecasting model. Results: All of the optimal forecasts for the next 12 months for two online shopping transactions maintain a pattern in which the slope increases linearly and steadily with a fixed seasonal change that has been subjected to seasonal fluctuations. Conclusions: It can be confirmed that the mobile shopping transactions is much larger than the internet shopping transactions for the increase in trend and seasonality in the future.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.173-180
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• 2023

Smart factory companies are installing various sensors in production facilities and collecting field data. However, there are relatively few companies that actively utilize collected data, academic research using field data is actively underway. This study seeks to develop a model that detects anomalies in the process by analyzing spindle power data from a company that processes shafts used in automobile throttle valves. Since the data collected during machining processing is time series data, the model was developed through unsupervised learning by applying the Holt Winters technique and various deep learning algorithms such as RNN, LSTM, GRU, BiRNN, BiLSTM, and BiGRU. To evaluate each model, the difference between predicted and actual values was compared using MSE and RMSE. The BiLSTM model showed the optimal results based on RMSE. In order to diagnose abnormalities in the developed model, the critical point was set using statistical techniques in consultation with experts in the field and verified. By collecting and preprocessing real-world data and developing a model, this study serves as a case study of utilizing time-series data in small and medium-sized enterprises.