兰州大学机构库 >数学与统计学院
基于SARIMA和BP神经网络的时间序列组合预测模型研究
Alternative TitleStudy on Combination Forecasting Model for Time Series Based on SARIMA and BP Neural Network
梁德阳
Thesis Advisor牛明飞
2014-06-01
Degree Grantor兰州大学
Place of Conferral兰州
Degree Name硕士
Keyword时间序列 SARIMA模型 BP神经网络模型 粒子群优化算法 Adaboost算法
Abstract时间序列数据是经济金融、交通运输和工程管理等领域常见的数据,这些领域的许多理论与实践问题对时间序列的分析提出了迫切的要求。实际的时间序列受到许多不同因素的影响,包含了反映序列自身周期波动及序列之间关系的重要信息,需要我们认真研究这些因素的作用。自20世纪以来,不断有学者对时间序列进行专门分析,提出了多种模型,如SARIMA模型、X12-ARIMA模型、BP神经网络模型和灰色预测模型等等。这些工作使时间序列的理论和应用进一步深入发展。 对各种模型进行比较分析后,研究表明不同预测模型各有优劣,都反映了原始数据的部分信息。以经典模型中的SARIMA模型为例,它是一般的ARIMA过程在季节时间序列模型中的推广,具有很强的线性建模能力。BP神经网络模型是近来兴起的人工智能方法,由于具有良好的非线性建模预测能力而被用于解决时间序列问题。为了进一步提高预测的精度,充分发挥不同模型的优点,Bates等提出了组合模型的构想。本文将根据不同的思路去建立时间序列的组合预测模型。首先引入粒子群优化 (PSO) 算法,通过PSO算法来搜索组合模型中各单项预测模型的权重,提出了将SARIMA和BP神经网络进行组合的预测模型。其次,参考adaboost组合分类器算法,提出一个BP_Adaboost时间序列组合预测模型。本文应用中国社会消费品零售总额月度数据时间序列对建立的组合模型进行实证检验,并对不同模型的预测效果进行了对比,结果表明,组合预测模型优于单项预测模型,有较好的预测能力。
Other AbstractTime series are common in some areas of research such as finance, transportation and engineering fields, in which many theoretical and practical issues have an urgent need for the analysis of time series. The actual time series affected by many different factors includes the important information reflecting the relationship between the sequence and the sequence of their cycle fluctuations so we need to seriously study the role of these factors.Since the 1900s, scholars have been persistently making analysis of seasonal time series. They put forwards a variety of models, such as SARIMA model, X12-ARIMA model, BP neural network model, GM(1,1),and so on. These efforts make further development of the theory and application of the time series. After a comparative analysis of various models, we find that different prediction models have advantages and disadvantages and reflect some of the information of the original data. The SARIMA model, one of the classical models, is a generalization of the ARIMA model in seasonal time series and has a strong capability on linear modeling. BP neural network model, one of the artificial intelligence methods recently, is also used to solve the problem of time series because of its good ability on nonlinear model prediction. In order to improve the prediction accuracy further and make full use of the advantages of different models, Bates and other researchers put forwards the idea of combined model. Firstly, we introduce the particle swarm optimization (PSO) algorithm to search the optimal weight coefficients of each individual prediction model and propose the combination forecasting model based on SARIMA and BP neural network. Secondly, with reference to adaboost combined classified algorithm, a model based on BP_Adaboost is proposed for time series prediction. This paper applied the combined model to the analysis of time series of China's total retail sales of social consumer goods and the effects of different models are compared. The results showed that the combination forecasting model is superior to single prediction model and has better predictive ability
URL查看原文
Language中文
Document Type学位论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/225206
Collection数学与统计学院
Recommended Citation
GB/T 7714
梁德阳. 基于SARIMA和BP神经网络的时间序列组合预测模型研究[D]. 兰州. 兰州大学,2014.
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