计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 535-539.doi: 10.11896/JsJkx.190700126
马创1, 袁野2, 尤海生2
MA Chuang1, YUAN Ye2 and YOU Hai-sheng2
摘要: 粮食在农产品中扮演着举足轻重的地位,粮食产量一定程度决定了国家粮食供给能力及温饱安全水平,因此对粮食产量进行精准预测的研究具有重要的价值。鉴于粮食产量受多种复杂因素的影响具有极强的波动性和随机性,为提高粮食产量预测的准确性,针对我国粮食产量的特点,文中提出一种基于灰色模型与马尔可夫模型相融合的模型,用马尔可夫模型对灰色模型的预测值进行修正以达到对粮食产量进行周期性预测。通过选取我国2009年至2018年的粮食年产量数据(数据来源:国家数据统计局)进行分析研究。所提方法首先利用灰色模型对产量进行预测,计算预测误差,通过对误差序列利用灰色建模修正产量预测数据;其次,根据粮食年产量预测精度,将粮食年产量数据划分成若干状态,进而可求出各阶状态转移概率和状态转移概率矩阵;最后,通过建立新陈代谢后的灰色模型对粮食年产量进行预测得到预测结果,利用马尔可夫模型对预测结果进行残差值进行修正以达到提高粮食产量预测值精度。仿真实验分别将单一灰色模型和灰色马尔可夫模型的预测精度进行比较。结果表明,灰色模型预测值在2009-2013 年的年产量预测中误差小于 1.00%,但随着年份的增加,由于粮食年产量间的相互影响导致预测精度变差,2014-2018年的年产量预测误差均高于1.00%;灰色-马尔可夫模型年产量预测误差均小于0.30%,平均误差为 0.12%,相较于传统灰色模型及马尔可夫模型,其预测的准确率大幅度提高。
中图分类号:
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