A Heterogeneous Graph Enhanced LSTM Network for Hog Price Prediction Using Online Discussion

Ye, Kai; Piao, Yangheran; Zhao, Kaixuan; Cui, Xiaohui

doi:10.3390/agriculture11040359

Cited by 13 publications

(8 citation statements)

References 38 publications

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“…ARIMA model is used by researchers to forecast the price of agricultural products [9][10][11][12][13]. Ye K, Piao Y, et al [14] propose "Heterogeneous Graphenhanced LSTM (HGLTSM), which is a method that predicts weekly hog price".…”

Section: Methods Articlementioning

confidence: 99%

An Improved Prediction Model of Pig Price

Gong

Cao

2022

AJAAR

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In many agricultural products, pig price fluctuation has a significant impact on price level and consumer price index, so accurate prediction of pig price is of great significance for pig market research and production. In order to predict the price of pigs more accurately in the short term, Attention-LSTM prediction model (a short-term memory neural network based on attention mechanism) is established. The results show that: compared with the traditional LSTM forecasting model, the Attention-LSTM model has higher prediction accuracy, and this model has a good effect on the short-term prediction of pig prices.

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Section: Methods Articlementioning

confidence: 99%

An Improved Prediction Model of Pig Price

Gong

Cao

2022

AJAAR

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“…Meanwhile, the conclusions of these articles are usually very vague or ambiguous. According to their study results, some scholars thought the SVR, as well as its combined models, outperformed other models [34,35], some scholars thought the LSTM and its combined model outperformed other models [25,27,34], some scholars thought BPNN had better generalization ability than other models [5,11,13], Ref. [4] thought the DMA had better performance than other models, Ref.…”

Section: Literature Reviewmentioning

confidence: 99%

Monthly Pork Price Prediction Applying Projection Pursuit Regression: Modeling, Empirical Research, Comparison, and Sustainability Implications

Yu,

Liu,

Lai

2024

Sustainability

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The drastic fluctuations in pork prices directly affect the sustainable development of pig farming, agriculture, and feed processing industries, reducing people’s happiness and sense of gain. Although there have been extensive studies on pork price prediction and early warning in the literature, some problems still need further study. Based on the monthly time series data of pork prices and other 11 influencing prices (variables) such as beef, hog, piglet, etc., in China from January 2000 to November 2023, we have established a project pursuit auto-regression (PPAR) and a hybrid PPAR (H-PPAR) model. The results of the PPAR model study show that the monthly pork prices in the lagged periods one to three have an important impact on the current monthly pork price. The first lagged period has the largest and most positive impact. The second lagged period has the second and a negative impact. We built the H-PPAR model using the 11 independent variables (prices), including the prices of corn, hog, mutton, hen’s egg, and beef in lagged period one, the piglet’s price in lagged period six, and by deleting non-important variables. The results of the H-PPAR model show that the hog price in lagged period one is the most critical factor, and beef price and the other six influencing variables are essential factors. The model’s performance metrics show that the PPAR and H-PPAR models outperform approaches such as support vector regression, error backpropagation neural network, dynamic model average, etc., and possess better suitability, applicability, and reliability. Our results forecast the changing trend of the monthly pork price and provide policy insights for administrators and pig farmers to control and adjust the monthly pork price and further enhance the health and sustainable development of the hog farming industry.

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“…Regional fruit price forecasting is commonplace, and it is crucial to business decision-making in those areas. According to the characteristics of various agricultural products, the type of forecasting can be either long-term [2] or short-term [3][4][5][6]. However, for fruits, it is mostly short-term, based on seasonality, periodicity, and perishability.…”

Section: Literature Overviewmentioning

confidence: 99%

Characteristic mango price forecasting using combined deep-learning optimization model

Jin²,

Huang³

et al. 2023

PLoS ONE

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Accurate product price forecasting is helpful for scientific decision-making and precise industrial planning. As a characteristic fruit that drives regional development, mango price prediction is of great significance to several economies. However, owing to the strong volatility of mango prices, forecasting is vulnerable to uncertainties and is very challenging. In this study, a deep-learning combination forecasting model based on a back-propagation (BP) long short-term memory (LSTM) neural network is proposed. Using daily mango price data from a large fruit wholesale trading center in China from January 2nd, 2014, to April 18th, 2022, mango price changes are learned and predicted to support the fruit industry. The results show that the root mean-square error, mean absolute percentage error, and the R2 determination coefficient of the BP-LSTM combination model are 0.0175, 0.14%, and 0.9998, respectively. The prediction results of the combined model are better than those of the separate BP and LSTM models. Furthermore, it best fits the actual price profile and has better generalizability.

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A Heterogeneous Graph Enhanced LSTM Network for Hog Price Prediction Using Online Discussion

Cited by 13 publications

References 38 publications

An Improved Prediction Model of Pig Price

An Improved Prediction Model of Pig Price

Monthly Pork Price Prediction Applying Projection Pursuit Regression: Modeling, Empirical Research, Comparison, and Sustainability Implications

Characteristic mango price forecasting using combined deep-learning optimization model

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