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Deep-Learning-Based Price Prediction by Outlier Detection and Processing for Agricultural Commodity Prices
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농산물 가격의 특이치 탐지 및 처리를 통한 딥러닝 기반 가격 예측

논문 기본 정보

Type
Academic journal
Author
Chan Park (한국해양기상기술) Kyung-Soon Lee (전북대학교)
Journal
Digital Contents Society Journal of Digital Contents Society Vol.24 No.8 KCI Accredited Journals
Published
2023.8
Pages
1,899 - 1,906 (8page)
DOI
10.9728/dcs.2023.24.8.1899

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📌
Topic
본 연구는 농산물 가격 변동의 특이치 탐지 및 처리를 통해 가격 예측 모델의 성능을 향상시키는 것을 목표로 한다.
📖
Background
농산물 가격의 변동성은 여러 요인에 의해 영향을 받으며, 이는 농민의 소득과 소비자에게 미치는 비용에 큰 영향을 판단한다.
🔬
Method
본 연구에서는 농산물 가격 데이터의 계절성을 고려하여 사분위를 이용한 특이치 탐지 기법을 적용하고, 이후 LSTM 모델을 사용하여 가격 예측을 수행한다.
🏆
Result
특이치 탐지 및 처리를 통해 LSTM 모델을 적용했을 때, 평균 제곱근 오차(RMSE)와 평균 절대 비율 오차(MAPE)에서 가장 우수한 성능을 보였다.
Deep-Learning-Based Price Prediction by Outlier Detection and Processing for Agricultural Commodity Prices
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Research history (2)

2023

Deep-Learning-Based Price Prediction by Outlier Detection and Processing for Agricultural Commodity Prices
Chan Pa ,  k ,  Kyung-Soon Lee Journal of Digital Contents Society 2023.08 Academic journal

2020

Outlier detection in the agrifoods product price
Chan Pa ,  k ,  Kung-Soon Lee 한국정보과학회 학술발표논문집 2020.12 Proceeding
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Abstract· Keywords

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In this paper, we propose a deep-learning-based price prediction model by detecting and handling outliers for agricultural products. Outliers caused by biased data can lead to performance deterioration issues during the learning and prediction processes of a deep learning model. To overcome this issue, we present a method that divides the data into monthly intervals and utilizes the interquartile range to detect outliers, followed by transforming the time series data into a stationary form using the moving average method. In our experiments, the conventional long short-term memory deep learning model showed mean absolute percentage error (MAPE) values of 5.41, 4.15, and 5.55 for cabbage, radish, and onion, respectively, while the proposed method showed MAPE values of 4.15, 3.19, and 4.92, respectively. These results highlight the effectiveness of outlier detection and handling in enhancing the performance of deep-learning-based prediction models.
#이상치 탐지 #이상치 처리 #가격 예측 #농산물 가격 #딥러닝 모델 #Outlier Detection #Outlier Processing #Price Prediction #Agricultural Commodity #Deep Learning Model

AI Summary

Topic

본 연구는 농산물 가격 변동의 특이치 탐지 및 처리를 통해 가격 예측 모델의 성능을 향상시키는 것을 목표로 한다.

Background

농산물 가격의 변동성은 여러 요인에 의해 영향을 받으며, 이는 농민의 소득과 소비자에게 미치는 비용에 큰 영향을 판단한다.

Method

본 연구에서는 농산물 가격 데이터의 계절성을 고려하여 사분위를 이용한 특이치 탐지 기법을 적용하고, 이후 LSTM 모델을 사용하여 가격 예측을 수행한다.

Result

특이치 탐지 및 처리를 통해 LSTM 모델을 적용했을 때, 평균 제곱근 오차(RMSE)와 평균 절대 비율 오차(MAPE)에서 가장 우수한 성능을 보였다.

주요내용

Contents

요약
Abstract
Ⅰ. 서론
Ⅱ. 관련 연구
Ⅲ. 농산물 가격 특이치 탐지
Ⅳ. 특이치 처리를 통한 딥러닝 기반 가격 예측
Ⅴ. 실험 및 평가
VI. 결론
참고문헌

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