基于SARIMA-SVR组合模型的 空气质量指数预测

空气质量指数(AQI)在波动中既具有整体的时间序列线性特征和明显的季节性波动周期,又具有多种因素影响的不确定性,为了提高AQI的预测精度,基于Ri386 3.3.3和Matlab R2014a两种编程软件,提出了一种同时具有线性和非线性的复合特征的时间序列预测模型——SARIMA-SVR组合模型。以太原市2014年1月—2019年7月的AQI月均值数据为基础,利用SARIMA时间序列模型进行线性预测,利用SVR模型对残差进行非线性预测,加和得到组合预测模型的预测结果,分析比较SARIMA,SVR和SARIMA-SVR这3种模型的预测结果和平均绝对百分比误差。结果表明,组合预测模型发挥了2种模型各自的优势,相较于单一预测模型的预测结果而言,其预测精度更高,稳定性更好。通过此模型得到的空气质量预测结果不仅可为人们的日常生活提供指导,而且可为大气污染的防治工作提供科学依据和借鉴意义。     

金融危机研究模型与预测问题研究

本文回顾了金融危机形成理论的研究方法和研究模型,发现这些模型有两个重要的缺陷:即脱离了危机前宏观经济恶化的实际,同时把政府在经济恶化后的经济政策外生于模型之外,从而导致了危机理论只能解释危机而无法形成正确的预测。本文认为金融危机是市场缺陷在宏观经济恶化条件下的放大,是不适当的宏观经济政策的必然结果,现代金融条件下金融体系的脆弱性增加了金融危机爆发的频次和危害性。     

“十三五”期间我国经济——能源——环境系统变量发展预测

本文以动态可计算一般均衡模型为基础,2010年投入产出表延长表为基准数据,对“十三五”期间我国经济--能源--环境系统相关变量进行了预测。分析结果显示：“十三五”期间我国经济将实现新常态下的稳定适宜增长,GDP增速保持在65%左右,产业结构进一步优化,第三产业比重明显上升,第二产业明显下降。由于经济增长的原因,能源消费总量与碳排放总量依然较高,但能源消费结构进一步优化,碳排放强度明显降低,节能减排的形势依然比较严峻。     

Gamma-Minimax Prediction for the Multinomial Distribution

Characterizations of gamma-minimax predictors for the linear combinations of the unknown parameter and the random variable having the multinomial distribution under arbitrary squared error loss are established in two situations – when the sample size is fixed and when the sample size is a realization of a random variable. It is always assumed that the available vague prior information about the unknown parameter can be described by a class of priors whose vector of first moments belongs to a suitable convex and compact set. Several known gamma-minimax and minimax results can be obtained from the characterizations derived in the present paper.     

Jason与PowerLog联合处理技术在储层预测中的应用

研究采用PowerLog软件进行测井资料预处理,为反演打好基础。应用Jason软件算出波阻抗之后,利用测井统计的波阻抗和岩性的关系,把波阻抗数据体转换成岩性数据体,再用测井统计的波阻抗和孔隙度的关系,生成孔隙度数据体,最后在Jason软件的三维可视化模块中利用岩性数据体和孔隙度数据体进行储层提取。     

TS模糊模型在个股股价预测中的应用研究

个股价格短期内的变化量一般可通过股票技术指标间接反映出来,且这些指标间有着一定的相关性;另外,股票价格模型具有Takagi-Sugeno(TS)模糊模型所研究问题的非线性、时变性特点,基于此,本研究将TS模糊模型与分析出的股票常用技术指标相结合进行股价预测。结果显示预测出的价格与股票实际价格近似一致,精度高,因此研究具有一定的实用价值。     

水泥土搅拌桩复合地基承载力的灰色预测

详细阐述灰色模型的建立及调整过程,利用复合地基沉降实测数据,根据灰色系统理论,建立(1,1)模型,可模拟复合地基的P~s曲线,预测地基沉降的发展趋势,分析水泥土搅拌桩复合地基的承载性能。     

Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model

The study of climatic variables that govern the Indian summer monsoon has been widely explored. In this work, we use a non-linear deep learning-based feature reduction scheme for the discovery of skilful predictors for monsoon rainfall with climatic variables from various regions of the globe. We use a stacked autoencoder network along with two advanced machine learning techniques to forecast the Indian summer monsoon. We show that the predictors such as the sea surface temperature and zonal wind can predict the Indian summer monsoon one month ahead, whereas the sea level pressure can predict ten months before the season. Further, we also show that the predictors derived from a combination of climatic variables can outperform the predictors derived from an individual variable. The stacked autoencoder model with combined predictors of sea surface temperature and sea level pressure can predict the monsoon (June-September) two months ahead with a 2.8% error. The accuracy of the identified predictors is found to be superior to the state-of-the-art predictions of the Indian monsoon.     

RMS5000产品可靠性信息管理系统设计

RMS5000产品可靠性信息管理系统是一套基于产品可靠性设计理念的信息管理系统,该系统从可靠性设计流程标准化、潜在缺陷分析、可靠性应力分析、可靠性测试流程标准化、设计评审审核、经验数据累计等几方面为用户提供了一整套技术有效、管理科学的可靠性技术体系。该系统旨在规范可靠性设计和测试流程、提高可靠性设计效率和产品质量。本文通过图文相结合的方式向用户全面阐述了该系统的设计理念、设计架构、产品开组成以及技术特点。希望能通过本文的介绍,向大家展示一个基于可靠性的产品开发体系模型,给设计人员在产品可靠性设计方面提供更多的帮助。     

Three Corrected Score Tests for Generalized Linear Models with Dispersion Covariates

We develop three corrected score tests for generalized linear models with dispersion covariates, thus generalizing the results of Cordeiro , Ferrari and Paula (1993) and Cribari-Neto and Ferrari (1995) . We present, in matrix notation, general formulae for the coefficients which define the corrected statistics. The formulae only require simple operations on matrices and can be used to obtain analytically closed-form corrections for score test statistics in a variety of special generalized linear models with dispersion covariates. They also have advantages for numerical purposes since our formulae are readily computable using a language supporting numerical linear algebra. Two examples, namely, iid sampling without covariates on the mean or dispersion parameter oand one-way classification models, are given. We also present some simulations where the three corrected tests perform better than the usual score test, the likelihood ratio test and its Bartlett corrected version. Finally, we present a numerical example for a data set discussed by Simonoff and Tsai (1994) .