|Table of Contents|

Predicting Chinese Soybean Price Based on APSO_SVR(PDF)

《大豆科学》[ISSN:1000-9841/CN:23-1227/S]

Issue:
2017年04期
Page:
632-638
Research Field:
Publishing date:

Info

Title:
Predicting Chinese Soybean Price Based on APSO_SVR
Author(s):
HE Peng-fei; LI Jing;  ZHANG Dong-qing
（College of Engineering，Nanjing Agricultural University，Nanjing 210031，China）
Keywords:
SVR predicting model;  Adaptive;  PSO;  Soybean
PACS:
-
DOI:
10.11861/j.issn.1000-9841.2017.04.0632
Abstract:
Soybean price was influenced by many factors, such as soybean imports, domestic soybean outputs, consumer price index etc. The characteristics of soybean price is non-linearity, randomness etc. The fluctuation of soybean price would influence farming structure and national policy of soybean. Exact predicting soybean price is significant for farmers and soybean policy. Support vector machine was widely used in nonlinear time series because of its superior search capability and high accuracy. In this paper, SVR model optimized with adaptive particle swarm optimization (APSO) was used to predict soybean price. In this model, the data was mapped to high-dimensional space from real space. The linear regression function was constructed in the high dimensional space to distinguish the data relations in the real space. The parameters of SVR model was optimized with particle swarm optimization (PSO), but the PSO was usually trapped local optimization results.Therefore, adaptive strategy of fitness mutation and inertia weight updated was used to structure APSO. The data of soybean price from Jan- 2009 to Dec. 2016 were used to forecast. The results indicated that APSO and SVR model was accurate and effective.The SVR model can accurately reflect future trend of soybean price and provide decision basis for soybean farmers and soybean businessman.

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Last Update: 2017-08-14