온라인 리테일러에서 수요 패턴 예측을 위한 컨볼류션 LSTM 적용 방안
Application of Convolutional LSTM for Predicting Demand Patterns in Online Retailers
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Abstract
Demand pattern forecasting would be surely one of significant modules for successful and profitable on-line retail business. Our work studies the mechanism for demand pattern forecasting at the on-line retailers in a context of e-commerce business. This paper deals with demand pattern forecasting on a basis of time series prediction. We review papers about demand forecasting in a lot of application areas and suggest a new approach to time series prediction using Convolutional LSTM by generating the images for corresponding demand patterns. And it is observed that our proposed approach could elicit more accurate results than the current existing techniques such as seasonal ARIMA and Holt-Winters Technique. Numerical studies using the data for an e-commerce company have been done in order to validate the performance of the suggested demand pattern forecasting mechanism using the Convolutional LSTM. Finally, we provide the meaningful measures ensuring the usefulness and applicability of the proposed demand pattern forecasting mechanism.
참고문헌 (21)
- Creswel, A., "On denoising autoencoders trained to minimise binary cross-entropy", arXiv, ~ (2017)
- "Understanding LSTM Networks" (2015)[URL]
- Jung, D, "Prediction of the number of domestic breeding hog Using Winters Model", pp.2004
- 이흥석, "LSTM 기법을 이용한 풍력발전 출력 예측에 관한 연구", 전기학회논문지, 69(8), pp.1157~1164 (2020), 대한전기학회
- Kim, H., "Prediction of Time Series Data Using LSTM", GRU Recurrent Neural Network, Korea National Open University, 2017
- Hochreiter, S., "Long short-term memory", Neural Computation, 9(8), pp.1735~1780 (1997)
- 정혜린, "인공지능 기반 수요예측 기법의 리뷰", 응용통계연구, 32(6), pp.795~835 (2019), 한국통계학회
- 송인성, "예측 기반의 물류관리시스템 최적 저장위치 결정모형", 한국SCM학회지, 15(1), pp.1~8 (2015), 한국SCM학회
- 이재민, "계절성을 감안한 ARIMA 모형을 이용한 교통수요 동태적 변화 연구", 대한교통학회지, 28(5), pp.139~155 (2011), 대한교통학회
- 김종태, "홀트-윈터스 가법모형에 의한 전국 학생수 예측", 한국데이터정보과학회지, 20(4), pp.685~694 (2009), 한국데이터정보과학회
- Nenni, M. E., "Demand forecasting in the fashion industry: A review", International Journal of Engineering Business Management (2013)
- 이선민, "LSTM과 GRU 딥러닝 IoT 파워미터 기반의 단기 전력사용량 예측", 한국인터넷방송통신학회 논문지, 19(5), pp.79~85 (2019), 한국인터넷방송통신학회[DOI]
- 김시연, "계절 ARIMA 모형을 이용한 104주 주간 최대 전력수요예측", 조명.전기설비학회논문지, 28(1), pp.50~56 (2014), 한국조명.전기설비학회
- Chaudhuri, T., "Artificial Neural Network and Time Series Modeling Based Approach to Forecasting the Exchange Rate in a Multivariate Framework", Journal of Insurance and Financial Management, 1(5), pp.92~123 (2016)
- Weng, T., "Supply chain sales forecasting based on lightGBM and LSTM combination model", Industrial Management & Data Systems, 120(2), pp.265~279 (2019)
- Shi, X., "Convolutional LSTM network: A machine learning approach for precipitation nowcasting", Advances in neural information processing systems (NIPS2015), 28, pp.802~810 (2015)
- Ai, Y., "A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system", Neural Computing and Applications, 31, pp.1665~1677 (2018)
- Chang, Y., "A Seasonal ARIMA Model of Tourism Forecasting : The Case of Taiwan", Asia Pacific Journal of Tourism Research, 15(2), pp.215~221 (2010)
- Wu, Y., "Improved EEMD-based crude oil price forecasting using LSTM networks", Physica A : Statistical Mechanics and its Applications, 516, pp.114~124 (2019)
- "Established a cutting-edge distribution center in Gimcheon-si" (2020)[URL]
- "Online Retail Data Set" (2015)[URL]