A Garlic-Price-Prediction Approach Based on Combined LSTM and GARCH-Family Model

Wang, Yan; Liu, Pingzeng; Zhu, Ke; Liu, Lining; Zhang, Yan; Guang-li, XU

doi:10.3390/app122211366

Cited by 9 publications

(5 citation statements)

References 48 publications

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“…(2) In terms of the similarities and differences between this study and previous studies, in previous studies the price research of many agricultural products, including garlic, mainly focused on the price volatility characteristics [35][36][37][38][39] and the specific value prediction [40][41][42][43]. This study is the first to use the new data features constructed using decomposing sequences and the volatility features to predict the price fluctuation of garlic.…”

Section: Discussionmentioning

confidence: 94%

Prediction of Weekly Price Trend of Garlic Based on Classification Algorithm and Combined Features

Sun,

Meng,

Zhang

et al. 2024

Horticulturae

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To promote the sustainable development of the garlic industry and provide a reference for the prediction of agricultural product price trends, this study used the garlic price in Jinxiang, China as the research object. First, the feature combination De was obtained by extracting the sequence obtained using VMD decomposition. Then, the De_Vo combined feature was constructed by combining the volatility feature Vo. Classification algorithms, such as logistic regression, SVM, and XGBoost, were used to classify and predict the garlic price trend. The results showed that the prediction results based on the combined features were better than those based on the single De or Vo features. In the binary classification prediction, the accuracy values for LR, SVM, and XGBoost were 62.6%, 71.4%, and 72.9%, respectively. Among them, the XGBoost algorithm performed better than the LR and SVM algorithms in the three-class, four-class, and five-class predictions.

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Section: Discussionmentioning

confidence: 94%

Prediction of Weekly Price Trend of Garlic Based on Classification Algorithm and Combined Features

Sun,

Meng,

Zhang

et al. 2024

Horticulturae

Self Cite

View full text Add to dashboard Cite

show abstract

“…LSTM models based on mean absolute error and mean-square error criteria tend to show high accuracy with time-series predictions. In terms of agricultural price forecasting, Fang Xueqing et al [ 7 ] made short-term price forecasts for Fuji apples using the ensemble empirical mode decomposition LSTM, and Wang Xiaolei et al [ 23 ] made apple price predictions based on an LSTM that used a generalized autoregressive conditional heteroskedasticity method. In terms of agricultural product price forecasting, it is important to note that the selection focus of forecasting methods has gradually shifted from traditional regression methods [ 24 , 25 ] to deep-learning models [ 26 ].…”

Section: Related Literaturementioning

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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“…However, Jian Wei et al (2019) believe that although econometric models such as GARCH can effectively capture the fluctuations in carbon prices, they cannot accurately adapt to the nonlinear and non-stationary characteristics of carbon prices [25]. The use of these models alone has certain prediction limitations, which do not fully consider the influence of time series features and the special redundancy problem in data on trend prediction, resulting in weak prediction ability (Wang Xiaolei et al, 2021) [26].…”

Section: Introductionmentioning

confidence: 99%

Fluctuations and Forecasting of Carbon Price Based on A Hybrid Ensemble Learning GARCH-LSTM-Based Approach: A Case of Five Carbon Trading Markets in China

Liu,

Zhang,

Wang

et al. 2024

Sustainability

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Carbon trading risk management and policy making require accurate forecasting of carbon trading prices. Based on the sample of China’s carbon emission trading pilot market, this paper firstly uses the Augmented Dickey–Fuller test and Autoregressive conditional heteroscedasticity model to test the stationarity and autocorrelation of carbon trading price returns, uses the Generalized Autoregressive Conditional Heteroscedasticity family model to analyze the persistence, risk and asymmetry of carbon trading price return fluctuations, and then proposes a hybrid prediction model neural network (generalized autoregressive conditional heteroscedasticity–long short-term memory network) due to the shortcomings of GARCH models in carbon price fluctuation analysis and prediction. The model is used to predict the carbon trading price. The results show that the carbon trading pilots have different degrees of volatility aggregation characteristics and the volatility persistence is long, among which only the Shanghai and Beijing carbon trading markets have risk premiums. The other pilot returns have no correlation with risks, and the fluctuations of carbon trading prices and returns are asymmetrical. The prediction results of different models show that the root mean square error (RMSE) of Hubei, Shenzhen and Shanghai carbon trading pilots based on the GARCH-LSTM model is significantly lower than that of the single GARCH model, and the RMSE values are reduced by 0.0006, 0.2993 and 0.0151, respectively. The RMSE in the three pilot markets improved by 0.0007, 0.3011 and 0.0157, respectively, compared to the standalone LSTM model. At the same time, compared with the single model, the GARCH-LSTM model significantly increased the R^2 value in Hubei (0.2000), Shenzhen (0.7607), Shanghai (0.0542) and Beijing (0.0595). Therefore, compared with other models, the GARCH-LSTM model can significantly improve the prediction accuracy of carbon price and provide a new idea for scientifically predicting the fluctuation of financial time series such as carbon price.

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A Garlic-Price-Prediction Approach Based on Combined LSTM and GARCH-Family Model

Cited by 9 publications

References 48 publications

Prediction of Weekly Price Trend of Garlic Based on Classification Algorithm and Combined Features

Prediction of Weekly Price Trend of Garlic Based on Classification Algorithm and Combined Features

Characteristic mango price forecasting using combined deep-learning optimization model

Fluctuations and Forecasting of Carbon Price Based on A Hybrid Ensemble Learning GARCH-LSTM-Based Approach: A Case of Five Carbon Trading Markets in China

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