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A Multi-step Time Series Forecasting Model for Mid-to-Long Term Agricultural Price Prediction

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2023, 28(2), pp.201-207
  • DOI : 10.9708/jksci.2023.28.02.201
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : December 30, 2022
  • Accepted : January 31, 2023
  • Published : February 28, 2023

Jonghyun Park 1 LimYeongWoo 1 Do Hyun Lim 1 Yunsung Choi 2 Ahn, Hyunchul 1

1국민대학교
2(주)온투인

Accredited

ABSTRACT

In this paper, we propose an optimal model for mid to long-term price prediction of agricultural products using LGBM, MLP, LSTM, and GRU to compare and analyze the three strategies of the Multi-Step Time Series. The proposed model is designed to find the optimal combination between the models by selecting methods from various angles. Prior agricultural product price prediction studies have mainly adopted traditional econometric models such as ARIMA and LSTM-type models. In contrast, agricultural product price prediction studies related to Multi-Step Time Series were minimal. In this study, the experiment was conducted by dividing it into two periods according to the degree of volatility of agricultural product prices. As a result of the mid-to-long-term price prediction of three strategies, namely direct, hybrid, and multiple outputs, the hybrid approach showed relatively superior performance. This study academically and practically contributes to mid-to-long term daily price prediction by proposing an effective alternative.

Citation status

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