Analysis of Price Fluctuation Characteristics and Influencing Factors of Garlic Based on HP Filter Method

Wu, Guojing; Liu, Pingzeng; Chen, Weijie; Han, Wei

doi:10.1007/978-3-030-00006-6_30

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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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