TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
LSTM-based Sales Forecasting Model |
| 영문제목(English Title) |
LSTM-based Sales Forecasting Model |
| 저자(Author) |
Jun-Ki Hong
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| 원문수록처(Citation) |
VOL 15 NO. 4 PP. 1232 ~ 1245 (2021. 04) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
In this study, prediction of product sales as they relate to changes in temperature is proposed. This model uses long short-term memory (LSTM), which has shown excellent performance for time series predictions. For verification of the proposed sales prediction model, the sales of short pants, flip-flop sandals, and winter outerwear are predicted based on changes in temperature and time series sales data for clothing products collected from 2015 to 2019 (a total of 1,865 days). The sales predictions using the proposed model show increases in the sale of shorts and flip-flops as the temperature rises (a pattern similar to actual sales), while the sale of winter outerwear increases as the temperature decreases.
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| 키워드(Keyword) |
Deep Learning
Long Short-Term Memory(LSTM)
Sales Forecasting
Weather
Time Series Prediction
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| 파일첨부 |
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