A Novel Bézier LSTM Model: A Case Study in Corn Analysis

Accurate prediction of agricultural product prices is instrumental in providing rational guidance for agricultural production planning and the development of the agricultural industry. By constructing an end-to-end agricultural product price prediction model, incorporating a segmented Bézier curve f...

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Published inMathematics (Basel) Vol. 12; no. 15; p. 2308
Main Authors Zhao, Qingliang, Chen, Junji, Feng, Xiaobin, Wang, Yiduo
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2024
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ISSN2227-7390
2227-7390
DOI10.3390/math12152308

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Abstract Accurate prediction of agricultural product prices is instrumental in providing rational guidance for agricultural production planning and the development of the agricultural industry. By constructing an end-to-end agricultural product price prediction model, incorporating a segmented Bézier curve fitting algorithm and Long Short-Term Memory (LSTM) network, this study selects corn futures prices listed on the Dalian Commodity Exchange as the research subject to predict and validate their price trends. Firstly, corn futures prices are fitted using segmented Bézier curves. Subsequently, the fitted price sequence is employed as a feature and input into an LSTM network for training to obtain a price prediction model. Finally, the prediction results of the Bézier curve-based LSTM model are compared and analyzed with traditional LSTM, ARIMA (Autoregressive Integrated Moving Average Model), VMD-LSTM, and SVR (Support Vector Regression) models. The research findings indicate that the proposed Bézier curve-based LSTM model demonstrates significant predictive advantages in corn futures price prediction. Through comparison with traditional models, the effectiveness of this model is affirmed. Consequently, the Bézier curve-based LSTM model proposed in this paper can serve as a crucial reference for agricultural product price prediction, providing effective guidance for agricultural production planning and industry development.
AbstractList Accurate prediction of agricultural product prices is instrumental in providing rational guidance for agricultural production planning and the development of the agricultural industry. By constructing an end-to-end agricultural product price prediction model, incorporating a segmented Bézier curve fitting algorithm and Long Short-Term Memory (LSTM) network, this study selects corn futures prices listed on the Dalian Commodity Exchange as the research subject to predict and validate their price trends. Firstly, corn futures prices are fitted using segmented Bézier curves. Subsequently, the fitted price sequence is employed as a feature and input into an LSTM network for training to obtain a price prediction model. Finally, the prediction results of the Bézier curve-based LSTM model are compared and analyzed with traditional LSTM, ARIMA (Autoregressive Integrated Moving Average Model), VMD-LSTM, and SVR (Support Vector Regression) models. The research findings indicate that the proposed Bézier curve-based LSTM model demonstrates significant predictive advantages in corn futures price prediction. Through comparison with traditional models, the effectiveness of this model is affirmed. Consequently, the Bézier curve-based LSTM model proposed in this paper can serve as a crucial reference for agricultural product price prediction, providing effective guidance for agricultural production planning and industry development.
Audience Academic
Author Zhao, Qingliang
Chen, Junji
Feng, Xiaobin
Wang, Yiduo
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SubjectTerms Accuracy
Agriculture
Algorithms
ARIMA
Autoregressive models
Back propagation
Bézier curve
Case studies
Commodity futures
Corn
Curve fitting
Curves
Data smoothing
Design
Effectiveness
Farm produce
Forecasting
Forecasts and trends
Industrial development
Localization
LSTM
Methods
Neural networks
Prediction models
Predictions
price forecast
Prices
Production planning
Regression models
Statistical analysis
Support vector machines
SVR
Time series
Trends
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Title A Novel Bézier LSTM Model: A Case Study in Corn Analysis
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