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    ARTICLE

    Research on Hybrid Model of Garlic Short-term Price Forecasting based on Big Data

    Baojia Wang1, Pingzeng Liu1,*, Zhang Chao1, Wang Junmei1, Weijie Chen1, Ning Cao2, Gregory M.P. O’Hare3, Fujiang Wen1

    CMC-Computers, Materials & Continua, Vol.57, No.2, pp. 283-296, 2018, DOI:10.32604/cmc.2018.03791

    Abstract Garlic prices fluctuate dramatically in recent years and it is very difficult to predict garlic prices. The autoregressive integrated moving average (ARIMA) model is currently the most important method for predicting garlic prices. However, the ARIMA model can only predict the linear part of the garlic prices, and cannot predict its nonlinear part. Therefore, it is urgent to adopt a method to analyze the nonlinear characteristics of garlic prices. After comparing the advantages and disadvantages of several major prediction models which used to forecast nonlinear time series, using support vector machine (SVM) model to predict… More >

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