Corn Price Prediction Based on Time Series SVR Model

doi:10.11924/j.issn.1000-6850.casb20191200938

Abstract

Abstract:

The study aims to establish a short-term corn price forecasting model by using time series and support vector regression (SVR) theory, to provide technical support and decision-making basis for corn monitoring and early warning. Based on the non-linear characteristics of corn price series fluctuations, this study took the monthly data of Henan Province from January 1, 2010 to June 30, 2019 as the research object, and combined a chaotic time series with SVR theory, to study a kind of short-term corn price prediction model. The price sequence was processed by the phase space reconstruction method, the model was trained with the reconstructed data, and the grid cross validation (GridSearchCV) was used to optimize the research model. A corn price prediction model was obtained based on time series support vector regression. By analyzing the prediction results of the research model, it is showed that the prediction value of the research model is closer to the true value, and its average relative error (MRE) and root mean square error (RMSE) is 0.006 and 20.57, respectively, which are superior to the prediction results of the traditional support vector regression model, with more accuracy.

Key words: corn price, prediction model, support vector regression, phase space reconstruction, time series

CLC Number:

Zhang Baowen, Wang Chuan, Yang Chunying, Wang Laigang. Corn Price Prediction Based on Time Series SVR Model[J]. Chinese Agricultural Science Bulletin, 2020, 36(31): 115-120.

Figures/Tables 6

名称	表达式
线性核	$κ (x, y) = x T y + c$
多项式核	$κ (x, y) = (a x T y) d$
径向基核函数	$κ (x, y) = exp (- γ x - y 2)$
Sigmoid核	$κ (x, y) = tan h (β x T y + θ)$

名称	表达式
线性核	$κ (x, y) = x T y + c$
多项式核	$κ (x, y) = (a x T y) d$
径向基核函数	$κ (x, y) = exp (- γ x - y 2)$
Sigmoid核	$κ (x, y) = tan h (β x T y + θ)$

序号	真实值/(元/t)	传统SVR模型		GSCV-SVR模型		基于时间序列SVR模型
序号	真实值/(元/t)	预测值/(元/t)	相对误差(RE)	预测值/(元/t)	相对误差(RE)	预测值/(元/t)	相对误差(RE)
1	1685.000	1663.000	0.013	1682.000	0.002	1688.833	0.002
2	1667.778	1698.000	0.018	1670.000	0.001	1725.291	0.034
3	1620.000	1683.880	0.039	1665.374	0.028	1620.100	0.000
4	1628.182	1634.861	0.004	1619.661	0.005	1627.169	0.001
5	1658.214	1644.497	0.008	1636.758	0.013	1721.461	0.038
6	1726.875	1675.387	0.030	1665.939	0.035	1727.007	0.000
7	1757.500	1746.051	0.007	1736.281	0.012	1755.612	0.001
8	1827.679	1776.178	0.028	1759.526	0.037	1827.049	0.000
9	1870.000	1848.683	0.011	1841.306	0.015	1872.285	0.001
10	1870.000	1890.638	0.011	1882.401	0.007	1872.285	0.001
11	1850.000	1889.026	0.021	1873.450	0.013	1849.547	0.000
12	1850.000	1868.108	0.010	1847.418	0.001	1849.547	0.000
13	1845.789	1868.875	0.013	1851.522	0.003	1846.541	0.000
14	1840.000	1864.470	0.013	1846.071	0.003	1840.310	0.000
15	1868.333	1858.570	0.005	1839.461	0.015	1868.104	0.000
16	1916.667	1888.437	0.015	1877.597	0.020	1935.279	0.010
17	1901.765	1937.917	0.019	1936.164	0.018	1903.316	0.001
18	1884.286	1920.289	0.019	1905.228	0.011	1909.223	0.013
19	1880.000	1902.665	0.012	1885.417	0.003	1900.987	0.011
20	1889.444	1898.911	0.005	1884.237	0.003	1911.900	0.012
21	1890.000	1908.964	0.010	1896.717	0.004	1911.738	0.012
22	1906.667	1909.156	0.001	1895.982	0.006	1907.163	0.000
23	1947.895	1926.560	0.011	1916.983	0.016	1950.488	0.001

评价模型	MRE	RMSE
传统SVR模型	0.014	29.436
GSCV-SVR模型	0.012	28.085
基于时间序列SVR模型	0.006	20.574

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