【摘要】 传统的ARIMA时间序列分析方法是基于线性技术来进行时序预测,而对非线性数据的处理不尽合理,效果欠佳;而影响电力物资需求的因素非常多,绝大多数的物资序列通常既包含了线性时序的部分,又包含了非线性时序的成分.本文提出在ARIMA对电力物资需求预测的基础上,融合BP神经网络进行误差修正,以全面提取物资序列中的复合特征,提高电力物资的预测精度.实验结果表明,误差修正后的电力物资预测精度有了显著提高,可以为制定物资采购计划提供重要的数据支持.更多还原

【Abstract】 The traditional ARIMA time series analysis method is based on the linear technology to predict the time series,while its processing of nonlinear data is not reasonable with poor effect. There are many factors influencing the demand of power supply, and most of the material sequences usually contain both the linear time series and the nonlinear time series.In this paper, based on the ARIMA forecast, the BP neural network is combined with error correction to extract the composite features in the material sequence in order to improve the forecast precision of the electric power materials. The experimental results show that the accuracy of power supply forecasting with error correction can be improved significantly, which can provide important data support for material procurement plan.更多还原