在传统的组合预测模型中,利用的数据大多为结构化数据,然而在网络环境下,非结构化数据广泛存在,因此充分利用非结构化数据所提供的有效信息是预测中要解决的关键问题之一。针对上述问题,文章构建了基于非结构化数据的局部线性嵌入和鲸...在传统的组合预测模型中,利用的数据大多为结构化数据,然而在网络环境下,非结构化数据广泛存在,因此充分利用非结构化数据所提供的有效信息是预测中要解决的关键问题之一。针对上述问题,文章构建了基于非结构化数据的局部线性嵌入和鲸鱼优化算法的最小二乘支持向量回归(locally linear embedding-whale optimization algorithm-least squares support vector regression,LLE-WOA-LSSVR)碳价格组合预测模型,通过LLE算法对非结构化的高维数据进行降维处理,并利用LSSVR进行预测。考虑到LSSVR模型中参数的选取会对预测结果产生影响,引入WOA算法优化模型中的参数。碳价格预测的实例结果表明,LLE-WOA-LSSVR预测模型可行且有效。展开更多
Long-memory process has been widely studied in classical financial time series analysis,which has merely been reported in the field of interval-valued financial time series.The aim of this paper is to explore long-mem...Long-memory process has been widely studied in classical financial time series analysis,which has merely been reported in the field of interval-valued financial time series.The aim of this paper is to explore long-memory process in the prediction of interval-valued time series(IvTS).To model the long-memory process,two novel interval-valued time series prediction models named as interval-valued vector autoregressive fractionally integrated moving average(IV-VARFIMA)and ARFIMAX-FIGARCH were established.In the developed long-memory pattern,both of the short term and long-term influences contained in IvTS can be included.As an application of the proposed models,interval-valued form of WTI crude oil futures price series is predicted.Compared to current IvTS prediction models,IV-VARFIMA and ARFIMAX-FIGARCH can provide better in-sample and out-of-sample forecasts.展开更多
文摘在传统的组合预测模型中,利用的数据大多为结构化数据,然而在网络环境下,非结构化数据广泛存在,因此充分利用非结构化数据所提供的有效信息是预测中要解决的关键问题之一。针对上述问题,文章构建了基于非结构化数据的局部线性嵌入和鲸鱼优化算法的最小二乘支持向量回归(locally linear embedding-whale optimization algorithm-least squares support vector regression,LLE-WOA-LSSVR)碳价格组合预测模型,通过LLE算法对非结构化的高维数据进行降维处理,并利用LSSVR进行预测。考虑到LSSVR模型中参数的选取会对预测结果产生影响,引入WOA算法优化模型中的参数。碳价格预测的实例结果表明,LLE-WOA-LSSVR预测模型可行且有效。
基金supported by the Humanities and Social Sciences Research Youth Project of the Ministry of Education of China under Grant No.21YJCZH148the Natural Science Foundation of Anhui Province under Grant Nos.2108085MG239,2108085QG290,2008085QG334,and 2008085MG226+2 种基金the National Natural Science Foundation of China under Grant Nos.72001001,71901001,and 72071001the Provincial Natural Science Research Project of Anhui Colleges,China under Grant No.KJ2020A0004The teacher project of Anhui Ecology and Economic Development Research Center in 2021 under Grant No.AHST2021002.
文摘Long-memory process has been widely studied in classical financial time series analysis,which has merely been reported in the field of interval-valued financial time series.The aim of this paper is to explore long-memory process in the prediction of interval-valued time series(IvTS).To model the long-memory process,two novel interval-valued time series prediction models named as interval-valued vector autoregressive fractionally integrated moving average(IV-VARFIMA)and ARFIMAX-FIGARCH were established.In the developed long-memory pattern,both of the short term and long-term influences contained in IvTS can be included.As an application of the proposed models,interval-valued form of WTI crude oil futures price series is predicted.Compared to current IvTS prediction models,IV-VARFIMA and ARFIMAX-FIGARCH can provide better in-sample and out-of-sample forecasts.
