Crustal strain field in central japan, based on the wavelet analyzed GPS time-series data

In order to estimate the secular crustal deformation in central Japan, we adopted the wavelet technique to find and remove the data discontinuities associated with earthquakes and artificial errors, and also remove seasonal variations and white noises in the GPS time series data of daily co-ordinates obtained from the continuous GPS network, GEONET of GSI. The strain components were computed based on least-squares prediction method by using improved time series data.The motion velocities of GPS sites at uniform 7 km × 7 km grids were interpolated by adopting the empirical covariance function and then differentiated with respect to space to estimate strain rates.The estimated strain field shows that there existed a compressive regime in the entire region. Higher maximum shear strain rates of about 0.2 microstrain/a are shown in and around the Izu and Boso peninsulas, and Tokyo Bay. Relatively high maximum shear strain rates are also shown in the southern Tohoku district and another notable area along the coast of the Japan Sea,which nearly corresponds to the Shinanogawa seismic zone.     

Forecasting accounting data: A multiple time-series analysis

This paper examines the relative forecasting performance of multivariate time-series analysis. One hundred consecutive monthly observations for three accounting series were obtained from a manufacturing division of a large corporation. Regression, univariate time-series, transfer-function, and multiple time-series models were identified, estimated, and used to forecast each accounting series. The multiple time-series model yielded the smallest forecast variances.     

带有色观测噪声解耦Wiener跟踪滤波器

对带有色观测噪声的离散线性系统，应用现代时间序列分析方法，基于ARMA新息模型，提出了一种解耦Wiener滤波新方法，仿真例子说明了本方法的有效性。     

基于BP网络的我国工伤事故死亡率预测模型

应用MATLAB软件的神经网络工具箱建立BP网络模型,通过基于时间序列分析方法对1974～2003年全国工业企业事故死亡率进行预测模型的建立和检验.结果表明,用人工神经网络建立的预测模型预测我国工伤事故死亡率符合度高,效果较好.     

ARMA Models and the Box–Jenkins Methodology

The purpose of this paper is to apply the Box–Jenkins methodology to ARIMA models and determine the reasons why in empirical tests it is found that the post-sample forecasting the accuracy of such models is generally worse than much simpler time series methods. The paper concludes that the major problem is the way of making the series stationary in its mean (i.e. the method of differencing) that has been proposed by Box and Jenkins. If alternative approaches are utilized to remove and extrapolate the trend in the data, ARMA models outperform the models selected through Box–Jenkins methodology. In addition, it is shown that using ARMA models to seasonally adjusted data slightly improves post-sample accuracies while simplifying the use of ARMA models. It is also confirmed that transformations slightly improve post-sample forecasting accuracy, particularly for long forecasting horizons. Finally, it is demonstrated that AR(1), AR(2) and ARMA(1,1) models can produce more accurate post-sample forecasts than those found through the application of Box–Jenkins methodology.© 1997 John Wiley & Sons, Ltd.     

混沌时间序列在边坡位移预测中的应用

根据非线性边坡位移时间序列,运用相空间重构理论,建立了加权一阶局域法边坡位移预测模型、基于Lyapunov指数的边坡位移预测模型、基于神经网络的混沌时间序列边坡位移预测模型,对比分析了三个预测模型的特点。新龙寺滑坡预测研究表明,基于混沌时间序列预测方法具有很好的预测精度和广泛的适应性。     

基于神经树的时间序列预测

提出了一种新的神经树模型来进行时间序列预测。采用语法引导的遗传编程来进化神经树的结构以建立一个时间序列预测模型,并把它和基于神经网络的时间序列预测模型的性能进行比较,结果显示本文提出的神经树时间序列预测模型较神经网络模型有更高的可信度。     

一种时序数据多项式拟合加速方法

考虑到时序数据自身特点,特别是针对周期采样的时序数据,本文提出一种多项式拟合加速方法,讨论了平均分段和非平均分段两种情况下时序数据多项式拟合方法,通过重复利用部分矩阵的中间计算结果,大幅提高了多项式拟合的计算速度.实验结果表明,对于周期采样数据,该方法在平均分段和非平均分段时最多可分别获得约28倍和17倍计算加速.      

时序广义犹豫模糊软集及其决策方法

针对犹豫模糊软集的属性参数并非同等重要的情形,同时考虑到时间的变化对决策会产生影响,在犹豫模糊软集的基础上,提出了时序广义犹豫模糊软集的概念。基于时序广义犹豫模糊软集的概念,定义了其基本运算,分析了相应的运算结果,并讨论了运算性质。提出了时序广义犹豫模糊软集的一种决策方法,给出了具体的决策步骤,并通过实例验证了该决策方法的合理性与可行性。     

Perspectives on forecasting research in accounting and finance

This paper offers some perspectives on forecasting research in accounting and finance. It is maintained that many common areas of forecasting research exist. Yet, most research has focused upon a particular (Box-Jenkins) technique and a particular (reported earnings) variable, virtually neglecting numerous other relevant forecasting research topics. This symposium issue includes papers which address several of these neglected research topics. The eight papers constituting the issue are classified into three categories: (1) univariate time-series modelling; (2) multivariate time-series modelling; and (3) comparison of experts' forecasts with those of statistical models. Following a summary of the papers, some suggestions for future research are offered.