장단기 기억 신경망을 사용한다변수 데이터 농산물 가격 예측 모델
Agricultural Product Price Prediction ModelUsing Multi-Variable Data Long Short Term Memory
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본 논문에서는 가격, 기후 요인, 수요, 수입량 등 다양한 변수를 데이터화한 후, LSTM(Long Short-Term Memory) 모델을 활용하여 농산물 가격을 예측하는 방법을 제안하였다. 시계열 데이터의 장기 의존성을 학습하는 LSTM 모델을 통해 예측 성능을 분석한결과, 다양한 데이터를 통합함으로써 기존 방법보다 성능이 향상되었음을 확인하였다. 또한, 종속 변수인 가격 데이터 없이 독립 변수들만을 활용한 예측에서도 의미 있는 성과를 거두어, 모델의 발전 가능성을 확인할 수 있었다. 더 나아가, 다변수 모델을 사용할경우 예측 성능이 더욱 개선될 수 있음을 알게 되었으며, 이러한 복합적인 접근이 배추 가격 예측의 정확도를 높이는 데 효과적임을시사한다
Abstract
This paper proposes a method for predicting agricultural product prices by utilizing various variables such asprice, climate factors, demand, and import volume as data, and applying the Long Short-Term Memory (LSTM)model. The analysis of prediction performance using the LSTM model, which learns the long-term dependencies oftime series data, showed that integrating diverse data improved performance compared to traditional methods. Furthermore, even when predicting without price data as a dependent variable, meaningful results were achievedusing only independent variables, indicating the potential for further model development. Moreover, it was foundthat using a multi-variable model could further enhance prediction performance, suggesting that this complexapproach is effective in improving the accuracy of cabbage price predictions.
참고문헌 (5)
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