基于突变论的会计信息系统评价

本文根据突变级数法的原理构建了会计信息系统评价指标体系,并结合实例运用突变级数法进行了分析,以期为评价会计信息系统提供借鉴。     

基于突变论的会计信息系统评价

本文根据突变级数法的原理构建了会计信息系统评价指标体系,并结合实例运用突变级数法进行了分析,以期为评价会计信息系统提供借鉴。     

级数收敛的判别方法

级数理论在数学分析中占有很重要的一席之地,而级数理论中,研究无穷级数的收敛性则相当的重要.仅由收敛原理来判别级数的敛散性,在实际问题中,往往是不可行的.本文中,主要介绍了比较判别法,柯西判别法,达朗贝尔判别法,拉阿比判别法,对数判别法,双比值判别法,高斯判别法,柯西积分判别法,对于常用的判别法,本文对其有效性做了简单的比较,从而能够使读者更加深入的了解和熟悉各种判别法的使用范围.     

论级数在分析学中的地位及应用

从分析学涉及到的课程的重要性及分析学的研究对象角度,阐述了级数在分析学中的重要地位;从非初等函数与解析函数的级数表示、微分方程的幂级数解法、用简单函数逼近复杂函数、积分值与无理数值的计算以及利用级数理论求极限问题等方面,综述了级数在分析学中的应用。     

逆散射原理及应用综述

本文介绍了当前勘探界正在研究的一种去除多次波及成像的新方法一应用逆散射级数来去除多次波，并适用于复杂构造地震波成像。其基本思路是：应用散射级数推导出逆散射级数，利用该级数中的高阶项，用于压制多次波和用于成像，取得了良好的效果。     

无穷级数求和方法研究

任何一个级数要么收敛要么发散,收敛的级数都是应该有和或和函数的。无穷级数求和方法很多,有很强的技巧性,文章就通过例子介绍无穷级数求和的若干方法,如裂项相消法、逐项微分或积分法、转化为函数项级数求解法、利用子列的极限等等,其目的是让学习者更加熟练地掌握无穷级数求和方法及技巧,从而进一步促进其对该知识的学习和理解。     

级数收敛的判别方法

级数理论在数学分析中占有很重要的一席之地，而级数理论中，研究无穷级数的收敛性则相当的重要。仅由收敛原理来判别级数的敛散性，在实际问题中，往往是不可行的。本文中，主要介绍了比较判别法，柯西判别法，达朗贝尔判别法，拉阿比判别法，对数判别法，双比值判别法，高斯判别法，柯西积分判别法，对于常用的判别法，本文对其有效性做了简单的比较，从而能够使读者更加深入的了解和熟悉各种判别法的使用范围。     

如何科学选择榨油机压榨级数

榨油机行业个别企业为了抢占市场,毫无科学依据地恶炒压榨概念,虚夸压榨级数能达到五六级甚至七八级,让用户真假难辨。为了保护广大用户的切身利益,抵制行业恶性竞争之风,文章从科学角度对榨油机压榨级数做了较为详细的分析,以期为大家科学选择压榨级数提供借鉴参考。     

基于突变级数法的中小型网络公司成长期价值评估

从平衡计分卡的四个角度出发,针对中小型网络公司所处成长期的经营特点,识别关键成功因素,构建了综合评价指标体系。引入并介绍了突变级数法的评价步骤,避免了主观权重赋值,更加科学客观。选取了10家创业板上市的中小型网络公司2013年年报作为对象,采用突变级数法进行评估、分析和验证。     

如何科学选择榨油机压榨级数

榨油机行业个别企业为了抢占市场，毫无科学依据地恶炒压榨概念，虚夸压榨级数能达到五六级甚至七八级，让用户真假难辨。为了保护广大用户的切身利益，抵制行业恶性竞争之风，文章从科学角度对榨油机压榨级数做了较为详细的分析，以期为大家科学选择压榨级数提供借鉴参考。     

正项级数收敛性的判别法的进一步研究

用初等方法深入研究了正向级数判别法,基于比值判别法和根值判别法判别法思想,研究了更高精度判别法,给出不断提高精度的判别法的构造思想以及一般性定理。     

Forecasting monthly and quarterly time series using STL decomposition

This paper is a re-examination of the benefits and limitations of decomposition and combination techniques in the area of forecasting, and also a contribution to the field, offering a new forecasting method. The new method is based on the disaggregation of time series components through the STL decomposition procedure, the extrapolation of linear combinations of the disaggregated sub-series, and the reaggregation of the extrapolations to obtain estimates for the global series. Applying the forecasting method to data from the NN3 and M1 Competition series, the results suggest that it can perform well relative to four other standard statistical techniques from the literature, namely the ARIMA, Theta, Holt-Winters’ and Holt’s Damped Trend methods. The relative advantages of the new method are then investigated further relative to a simple combination of the four statistical methods and a Classical Decomposition forecasting method. The strength of the method lies in its ability to predict long lead times with relatively high levels of accuracy, and to perform consistently well for a wide range of time series, irrespective of the characteristics, underlying structure and level of noise of the data.     

The theta model: a decomposition approach to forecasting

This paper presents a new univariate forecasting method. The method is based on the concept of modifying the local curvature of the time-series through a coefficient ‘Theta’ (the Greek letter θ), that is applied directly to the second differences of the data. The resulting series that are created maintain the mean and the slope of the original data but not their curvatures. These new time series are named Theta-lines. Their primary qualitative characteristic is the improvement of the approximation of the long-term behavior of the data or the augmentation of the short-term features, depending on the value of the Theta coefficient. The proposed method decomposes the original time series into two or more different Theta-lines. These are extrapolated separately and the subsequent forecasts are combined. The simple combination of two Theta-lines, the Theta=0 (straight line) and Theta=2 (double local curves) was adopted in order to produce forecasts for the 3003 series of the M3 competition. The method performed well, particularly for monthly series and for microeconomic data.     

套压修正声速提高动液面解释精度研究

本文提出一种用套压修正声速提高动液面解释精度的新方法,此方法严格地以动液面的定义为基础,综合考虑了反映动液面的各个要素,能够真实地反映动液面的真实状况,现场试验证明新方法完全克服了目前采用油管接箍法所存在的一系列问题。     

Exponential smoothing with a damped multiplicative trend

Multiplicative trend exponential smoothing has received very little attention in the literature. It involves modelling the local slope by smoothing successive ratios of the local level, and this leads to a forecast function that is the product of level and growth rate. By contrast, the popular Holt method uses an additive trend formulation. It has been argued that more real series have multiplicative trends than additive. However, even if this is true, it seems likely that the more conservative forecast function of the Holt method will be more robust when applied in an automated way to a large batch of series with different types of trend. In view of the improvements in accuracy seen in dampening the Holt method, in this paper we investigate a new damped multiplicative trend approach. An empirical study, using the monthly time series from the M3-Competition, gave encouraging results for the new approach at a range of forecast horizons, when compared to the established exponential smoothing methods.     

基于仿真方法的新型装备备件消耗预测研究

针对新型装备备件消耗历史数据较少，提出用仿真方法预测新型装备备件消耗。在分析影响装备备件消耗因素的基础上，做出一系列假设，用系统仿真的方法模拟装备的使用、故障和维修，以此预测备件的消耗。实例验证了用系统仿真方法预测新型装备备件消耗，其预测精度要高于传统预测方法。     

软土路基沉降处理研究

文章对特殊路段的软土特性进行了试验研究,对软土路基的变形计算、固结以及工后沉降计算给出计算方法,同时在高速公路建设中,针对软土路基的特殊性并结合工程应用提出了软土路基处理的一系列新方法。     

The reconstruction of index data in aggregative econometric models

Index data become inconsistent over time when publishing agencies change the method of aggregation. However, when they do so, they often republish some previously published data using the new method. It is shown that such overlapping data can be exploited to reconstruct the data series in an ‘optimal’ manner to provide for consistent series, using results from the theory of aggregation.     

繁忙机场高峰航班流量预测问题研究

为了提高繁忙机场的应急管理能力,为繁忙机场制定应急预案提供科学依据,本文提出了一种基于灰色模型和改进的支持向量回归机模型相结合的新方法,并对繁忙机场的非常态高峰航班流量的小样本进行了时间序列预测。     

Stochastic model specification search for Gaussian and partial non-Gaussian state space models

Model specification for state space models is a difficult task as one has to decide which components to include in the model and to specify whether these components are fixed or time-varying. To this aim a new model space MCMC method is developed in this paper. It is based on extending the Bayesian variable selection approach which is usually applied to variable selection in regression models to state space models. For non-Gaussian state space models stochastic model search MCMC makes use of auxiliary mixture sampling. We focus on structural time series models including seasonal components, trend or intervention. The method is applied to various well-known time series.