排序方式: 共有42条查询结果,搜索用时 15 毫秒
1.
针对混凝土坝变形分析预测的复杂性,应用相空间重构思想和融合建模理念,提出了一种基于SSA-LSTM-GF的混凝土坝变形分析预测方法。SSA-LSTM-GF方法利用奇异谱分析法(SSA)将变形实测数据序列分解为趋势分量、周期分量和剩余分量,并将剩余分量视为噪声分量予以剔除;采用长短期记忆神经网络(LSTM)模型和高斯拟合(GF)算法分别进行周期分量和趋势分量的分析预测,并将二者结果进行叠加重构,得到最终预测结果。实例验证结果表明,SSA可以达到较好的数据分解和消噪效果,LSTM模型针对周期分量的预测性能优越,GF算法能够很好地实现趋势分量的拟合预测和部分信息的挖掘提取,LSTM模型和GF算法的成果重构效果良好,SSA-LSTM-GF方法具有一定的可行性和应用价值。 相似文献
2.
教学效能感的结构:最小空间分析的运用 总被引:2,自引:0,他引:2
好的结构无疑是研究一个心理变量的基础.对于教学效能感的结构,不管是理论的解释还是实际的测量都受到不停的争议和质疑.该研究用474个样本检测了TES和中文教学效能感量表(俞国良和辛涛等,1995)的结构,指出了一般教学效能感(GTE)和个人教学效能感(PTE)两因子模型的局限性.按严格的程序翻译了俄亥俄州教师效能感量表(OSTES),并施测在相同的被试上,因素分析的结果呈单维结构.介绍并应用最小空间分析(SSA)重新探索OSTES的结构.最小空间分析的结果支持OSTES教学策略、课堂管理和学生参与三因子的存在,尤其是教学策略和课堂管理因子呈现出一条清晰的界线. 相似文献
3.
针对时间序列包含噪声以及单一模型可能存在预测表现不稳定的问题,本文提出了一个基于奇异谱分析(SSA)的集成预测模型,并将其运用于我国年度航空客运量的预测中.首先,采用SSA方法对原始时间序列进行分解和重构,得到一个剔除噪声的时间序列,然后将其作为单整自回归移动平均模型(ARIMA)、支持向量回归模型(SVR)、Holt-Winters方法(HW)等单一模型的输入并进行预测,接着再采用加权平均集成预测方法(WA)将三种单一模型的预测结果进行综合集成.通过与各单一模型、基于经验模态分解方法(EMD)的模型以及简单平均集成预测方法(SA)的预测结果进行对比发现,本文所建模型具有较高的预测精度和较稳定的预测表现.最后,采用本文的模型对我国2014-2016年年度航空客运量进行了预测. 相似文献
4.
Global surface temperature shows an increasing trend over the past 100 years.Anthropogenic and natural factors have been invoked to explain this warming, but their relative contribution and the spatial variability remain controversial.Based on a 2650-year temperature record from a stalagmite near Beijing, we explore the possible impacts of human activity on the warm season temperature in Beijing since the industrial era using the method of singular spectrum analysis (SSA).The results suggest that the warm s... 相似文献
5.
This paper describes the methodology of singular spectrum analysis (SSA) and demonstrate that it is a powerful method of time
series analysis and forecasting, particulary for economic time series. The authors consider the application of SSA to the
analysis and forecasting of the Iranian national accounts data as provided by the Central Bank of the Islamic Republic of
Iran.
This research was in part supported by a grant (No. 88/121230) from Institute for Trade Studies and Research (ITSR), Tehran,
Iran. 相似文献
6.
时间序列分维数提取算法的研究 总被引:6,自引:1,他引:6
混沌时间序列的分维数是描述其混沌特性的一个重要参数.这里介绍的改进的分维数提取算法是在传统的G-P关联维数分析算法的基础上,结合奇异谱分析技术,构造了基于奇异谱分析的关联维数提取算法,克服了原始G-P算法抗噪声干扰能力较差的缺点,同时具有可靠性较高、结果精度较理想、运算速度较快等优点 相似文献
7.
Modeling European industrial production with multivariate singular spectrum analysis: A cross‐industry analysis
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In this paper, an optimized multivariate singular spectrum analysis (MSSA) approach is proposed to find leading indicators of cross‐industry relations between 24 monthly, seasonally unadjusted industrial production (IP) series for German, French, and UK economies. Both recurrent and vector forecasting algorithms of horizontal MSSA (HMSSA) are considered. The results from the proposed multivariate approach are compared with those obtained via the optimized univariate singular spectrum analysis (SSA) forecasting algorithm to determine the statistical significance of each outcome. The data are rigorously tested for normality, seasonal unit root hypothesis, and structural breaks. The results are presented such that users can not only identify the most appropriate model based on the aim of the analysis, but also easily identify the leading indicators for each IP variable in each country. Our findings show that, for all three countries, forecasts from the proposed MSSA algorithm outperform the optimized SSA algorithm in over 70% of cases. Accordingly, this new approach succeeds in identifying leading indicators and is a viable option for selecting the SSA choices L and r, which minimizes a loss function. 相似文献
8.
基于SSA-ELM的大宗商品价格预测研究 总被引:2,自引:0,他引:2
随着经济全球化的发展,国际期货市场中各大类大宗商品价格波动剧烈,而全球经济形势不明朗以及货币政策不确定使得大宗商品期货价格难以被准确预测.本文选取玉米,原油,黄金分别作为大宗商品农产品类、能源类、金属类的代表对象,基于奇异谱分析方法(singular spectrum analysis,SSA),对商品期货价格进行分解,结合Kmeans动态聚类技术将分解量聚合成不同特征的价格序列,再采用具有优良特性的极限学习机算法(extreme learning machine,ELM)对模型进行训练,得到大宗商品期货价格预测模型.实证结果表明,采用序列分解聚类策略能够显著提高模型预测精度,在价格未来的整体水平和变动方向上都能达到较好的预测效果. 相似文献
9.
MENEZES Moises Lima de SOUZA Reinaldo Castro PESSANHA Jos Francisco Moreira 《系统科学与复杂性》2014,27(1):29-46
Singular spectrum analysis (SSA) is a technique that decomposes a time series into a set of components, such as, trend, harmonics, and residuals. Leaving out the residual components and adding up the others, the time series can be smoothed. This procedure has been used to model Brazilian electricity consumption and flow series. The PAR(p), periodic autoregressive models, has been broadly used in modelling energy series in Brazil. This paper presents an approach of this decomposition method, by fitting the PAR(p), considering its multivariate version known as multivariate SSA (MSSA). The method was applied to a vector of two wind speed series recorded at two locations in the Brazilian Northeast region. The obtained results, when compared to the univariate decomposition of each series, were far superior, showing that the spatial correlation between the two series were considered by MSSA decomposition stage. 相似文献
10.
极细颗粒黏土的比表面积测试与分析(英文) 总被引:5,自引:0,他引:5
软黏土如淤泥和淤泥质土的固相物质主要由极细粒径的黏土矿物和胶状物质组成,其力学性质、渗透性以及流变性等与颗粒的比表面积及表面电荷性质和密度密切相关.因而,比表面积测试对分析极细颗粒土的物理力学性质具有重要作用.采用乙二醇乙醚(EGME)吸附法和氮吸附(BET)法分别对膨润土、高岭土、石英和长石以及其混合土、深圳土、科威特土等极细颗粒土的比表面积进行测试和分析.比表面积测试结果表明,对于同样颗粒尺度的土情况,比表面积膨润土远大于高岭土,石英与长石相近;深圳土小于科威特土;随着膨润土相对含量的增加,试样的比表面积迅速增加;EGME法与BET法能够分别测出膨润土的总比表面积与外比表面积,高岭土的总比表面积与外比表面积相当接近,石英与长石的测试结果也比较接近.文中的比表面积测试和分析对深入了解影响软黏土的渗流固结与流变性等物理机制具有指导意义. 相似文献