TY - JOUR
AU - 
AB - Copyright © 2018 Tech Science Press CMC, vol.57, no.2, pp.283-296, 2018 Research on Hybrid Model of Garlic Short-term Price Forecasting based on Big Data 1 1, * 1 1 1 Baojia Wang , Pingzeng Liu , Zhang Chao , Wang Junmei , Weijie Chen , Ning 2 3 1 Cao , Gregory M.P. O’Hare and Fujiang Wen 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 the nonlinear part of garlic prices and establish ARIMA-SVM hybrid forecast model to predict garlic prices. The monthly average price data of garlic in 2010-2017 was used to test the effect of ARIMA model, SVM model and ARIMA-SVM model. The experimental results show that: (1) Garlic 
TI - Research on Hybrid Model of Garlic Short-term Price Forecasting based on Big Data
JF - Computers, Materials & Continua
DO - 10.32604/cmc.2018.03791
DA - 2018-01-01
UR - https://www.deepdyve.com/lp/unpaywall/research-on-hybrid-model-of-garlic-short-term-price-forecasting-based-aX0L5SqKDU
DP - DeepDyve
ER -