High-frequency compensation for seismic data based on adaptive generalized S transform

The low-pass fi ltering eff ect of the Earth results in the absorption and attenuation of the high-frequency components of seismic signals by the stratum during propagation.Hence,seismic data have low resolution.Considering the limitations of traditional high-frequency compensation methods,this paper presents a new method based on adaptive generalized S transform.This method is based on the study of frequency spectrum attenuation law of seismic signals,and the Gauss window function of adaptive generalized S transform is used to fi t the attenuation trend of seismic signals to seek the optimal Gauss window function.The amplitude spectrum compensation function constructed using the optimal Gauss window function is used to modify the time-frequency spectrum of the adaptive generalized S transform of seismic signals and reconstruct seismic signals to compensate for high-frequency attenuation.Practical data processing results show that the method can compensate for the high-frequency components that are absorbed and attenuated by the stratum,thereby eff ectively improving the resolution and quality of seismic data.

Covariance Estimation Using High-Frequency Data： An Analysis of Nord Pool Electricity Forward Data

The modeling of volatility and correlation is important in order to calculate hedge ratios, value at risk estimates, CAPM （Capital Asset Pricing Model betas）, derivate pricing and risk management in general. Recent access to intra-daily high-frequency data for two of the most liquid contracts at the Nord Pool exchange has made it possible to apply new and promising methods for analyzing volatility and correlation. The concepts of realized volatility and realized correlation are applied, and this study statistically describes the distribution （both distributional properties and temporal dependencies） of electricity forward data from 2005 to 2009. The main findings show that the logarithmic realized volatility is approximately normally distributed, while realized correlation seems not to be. Further, realized volatility and realized correlation have a long-memory feature. There also seems to be a high correlation between realized correlation and volatilities and positive relations between trading volume and realized volatility and between trading volume and realized correlation. These results are to a large extent consistent with earlier studies of stylized facts of other financial and commodity markets.

Vessel fusion tracking with a dual-frequency high-frequency surface wave radar and calibrated by an automatic identification system

High-frequency surface wave radar（HFSWR） and automatic identification system（AIS） are the two most important sensors used for vessel tracking.The HFSWR can be applied to tracking all vessels in a detection area,while the AIS is usually used to verify the information of cooperative vessels.Because of interference from sea clutter,employing single-frequency HFSWR for vessel tracking may obscure vessels located in the blind zones of Bragg peaks.Analyzing changes in the detection frequencies constitutes an effective method for addressing this deficiency.A solution consisting of vessel fusion tracking is proposed using dual-frequency HFSWR data calibrated by the AIS.Since different systematic biases exist between HFSWR frequency measurements and AIS measurements,AIS information is used to estimate and correct the HFSWR systematic biases at each frequency.First,AIS point measurements for cooperative vessels are associated with the HFSWR measurements using a JVC assignment algorithm.From the association results of the cooperative vessels,the systematic biases in the dualfrequency HFSWR data are estimated and corrected.Then,based on the corrected dual-frequency HFSWR data,the vessels are tracked using a dual-frequency fusion joint probabilistic data association（JPDA）-unscented Kalman filter（UKF） algorithm.Experimental results using real-life detection data show that the proposed method is efficient at tracking vessels in real time and can improve the tracking capability and accuracy compared with tracking processes involving single-frequency data.

房价影响因素的空间非一致性与差异化调控手段——基于Panel Data模型的实证研究

房地产价格受多种宏观经济因素的综合影响,不同城市的房价决定因素可能存在差异。文章将全国城市划分为四种级别,并选择17个一、二、三线样本城市,以货币供应量、CPI、GDP、城镇居民家庭人均可支配收入和社会固定资产投资额为解释变量,选取2002-2012年的季度数据,构建Panel Data模型,研究房价影响因素的空间非一致性,研究结果证明了空间非一致性的存在。基于此,对一、二、三线城市分别提出了差异性调控手段建议。

基于Panel Data模型的土地供应量对房价的影响研究

研究目的:定量反映土地供应量对房价的影响及其作用机制。研究方法:计量经济学方法。研究结果:一年前的土地供应量对房屋供应量的影响是正的,对房价的影响是负的,两者在统计上都是显著的,但土地供应量增加对降低房价的影响力非常小。两年前的土地供应量虽然影响房屋供应量,但却不影响房价。研究结论:影响中国当前房价的因素非常复杂,增减土地供应量对调控房价有一定作用,但必须同时有其他工具的辅助,否则其效果很可能不够显著。

Migration scheme for imaging offset VSP data within local phase space

The imaging of offset VSP data in local phase space can improve the image of the subsurface structure near the well.In this paper,we present a migration scheme for imaging VSP data in a local phase space,which uses the Gabor-Daubechies tight framebased extrapolator（G-D extrapolator） and its high-frequency asymptotic expansion to extrapolate wavefields and also delineates an improved correlation imaging condition in the local angle domain.The results for migrating synthetic and real VSP data demonstrate that the application of the high-frequency G-D extrapolator asymptotic expansion can effectively decrease computational complexity.The local angle domain correlation imaging condition can be used to weaken migration artifacts without increasing computation.

Super Resolution Perception for Improving Data Completeness in Smart Grid State Estimation

The smart grid is an evolving critical infrastructure,which combines renewable energy and the most advanced information and communication technologies to provide more economic and secure power supply services.To cope with the intermittency of ever-increasing renewable energy and ensure the security of the smart grid,state estimation,which serves as a basic tool for understanding the true states of a smart grid,should be performed with high frequency.More complete system state data are needed to support high-frequency state estimation.The data completeness problem for smart grid state estimation is therefore studied in this paper.The problem of improving data completeness by recovering highfrequency data from low-frequency data is formulated as a super resolution perception(SRP)problem in this paper.A novel machine-learning-based SRP approach is thereafter proposed.The proposed method,namely the Super Resolution Perception Net for State Estimation(SRPNSE),consists of three steps:feature extraction,information completion,and data reconstruction.Case studies have demonstrated the effectiveness and value of the proposed SRPNSE approach in recovering high-frequency data from low-frequency data for the state estimation.

Novel modelling strategies for high‑frequency stock trading data

Full electronic automation in stock exchanges has recently become popular,generat-ing high-frequency intraday data and motivating the development of near real-time price forecasting methods.Machine learning algorithms are widely applied to mid-price stock predictions.Processing raw data as inputs for prediction models(e.g.,data thinning and feature engineering)can primarily affect the performance of the prediction methods.However,researchers rarely discuss this topic.This motivated us to propose three novel modelling strategies for processing raw data.We illustrate how our novel modelling strategies improve forecasting performance by analyzing high-frequency data of the Dow Jones 30 component stocks.In these experiments,our strategies often lead to statistically significant improvement in predictions.The three strategies improve the F1 scores of the SVM models by 0.056,0.087,and 0.016,respectively.

Modelling Animal Activity as Curves: An Approach Using Wavelet-Based Functional Data Analysis

Temporal activity patterns in animals emerge from complex interactions between choices made by organisms as responses to biotic interactions and challenges posed by external factors. Temporal activity pattern is an inherently continuous process, even being recorded as a time series. The discreteness of the data set is clearly due to data-acquisition limitations rather than a true underlying discrete nature of the phenomenon itself. Therefore, curves are a natural representation for high-frequency data. Here, we fully model temporal activity data as curves integrating wavelets and functional data analysis, allowing for testing hypotheses based on curves rather than on scalar and vector-valued data. Temporal activity data were obtained experimentally for males and females of a small-bodied marsupial and modelled as wavelets with independent and identically distributed errors and dependent errors. The null hypothesis of no difference in temporal activity pattern between male and female curves was tested with functional analysis of variance (FANOVA). The null hypothesis was rejected by FANOVA and we discussed the differences in temporal activity pattern curves between males and females in terms of ecological and life-history attributes of the reference species. We also performed numerical analysis that shed light on the regularity properties of the wavelet bases used and the thresholding parameters.

政府大数据思维下宏观调控的理论分析

宏观调控是政府为解决信息不完全导致市场失灵问题所采取的政策措施。信息通信技术、互联网技术、大数据分析和人工智能等可以在一定程度上改善信息不完全的问题,这些技术正在驱动着政府运用大数据思维进行宏观调控。我们分析政府大数据思维及大数据思维下的宏观调控,首先要在理论上解读信息和大数据的关系,其次是说明政府大数据思维的形成过程,再次是解说政府宏观调控的操作程序和过程,最后对政府大数据思维下的宏观调控效用进行评价。大数据思维的本质仍然是因果思维,但大数据时代的因果逻辑思维模式与工业化时代的因果逻辑思维模式不是一回事。政府作为宏观调控的行为主体,从大数据思维进入大数据分析这一过程,包含着极其丰富的内容,可考虑从信息通信技术、大数据、互联网和人工智能等相互融合的角度对其逻辑分析框架展开探讨。有了这样的逻辑分析框架,我们可以描绘出政府对未来数字经济运行进行宏观调控有可能展现的一般图景。

卫生健康标准中关系型数据共现矩阵计算及SAS程序实现

目的设计卫生健康标准中关系型数据共现矩阵的求解路径及其SAS实现方法。方法文章以计算一组标准的起草单位共现矩阵为例,将“标准-起草单位”二维表格导入SAS(版本号:9.4),按照“两两相乘”的计算思路自行设计宏程序计算起草单位之间的共现矩阵。结果以计算一组标准的起草单位共现矩阵为例,采用自行构建的模拟数据进行演示。首先,导入宏循环起始的数据集,并计算共现频次C_(j,k);然后,导出为out数据集,进一步合并数据集,形成最终的共现矩阵。结论SAS宏程序计算标准中关系型数据共现矩阵具有灵活高效的优势,可用于社会网络分析和互动演进规律总结。

基于轨迹数据的快速路交织区拥堵演变特征研究

对快速路交织区拥堵演变过程中宏观交通流参数与交通状态的时变关系进行研究,可以为快速路交织区交通状态判别提供科学依据。利用YOLO算法从高清视频中提取车辆轨迹数据后,利用卡尔曼滤波对原始轨迹数据进行降噪平滑处理;对快速路交织区拥堵演变过程中速度、流量、密度等宏观交通流参数与交通状态进行时变分析,揭示快速路交织区宏观交通流参数在拥堵演变过程中的时变特征。结果表明,在快速路交织区交通状态判别时,融合平均行程速度和交通流密度等指标,可以有效提高交通状态判别精度。

网络搜索数据与我国GDP的关联机理分析

网络搜索数据是研究我国宏观经济现象的重要微观信息依据。本文从需求、供给与政策三方面选取和筛选关键词合成网络搜索指数,并与我国GDP进行相关性研究。结果表明:网络搜索指数与GDP的相关性较高,且两者存在长期均衡关系与短期误差修正机制,当GDP逐渐偏离均衡,将会以1~2个月的调整速度从非均衡态过渡到均衡态;网络搜索指数的增长对我国GDP有促进作用。

一种基于激光点云数据的微距栅格体积算法

针对规则格网算法难以满足激光点云模型高精度体积计算的问题,提出了一种基于激光雷达点云数据的微距栅格体积算法。该方法首先运用葛立恒凸包算法提取凸包点集,然后运用微距格网划分、高程插值和网格体积累加的方法计算体积。与规则格网法不同,这种算法充分利用激光雷达数据高密度点云特征,采用格网微分和增大插值半径的方法改善模型表面的连续性,进而提高计算精度。实验结果表明,微距栅格体积算法具有较好的时间复杂度和较高的计算精度,适宜于激光点云模型高精度体积计算。

An extended sparsemax-linearmoving model with application to high-frequency financial data

There continues to be unfading interest in developing parametric max-stable processes for modelling tail dependencies and clustered extremes in time series data.However,this comes with some difficulties mainly due to the lack of models that fit data directly without transforming the data and the barriers in estimating a significant number of parameters in the existing models.In thiswork,we study the use of the sparsemaxima ofmovingmaxima(M3)process.After introducing random effects and hidden Fréchet type shocks into the process,we get an extended maxlinear model.The extended model then enables us to model cases of tail dependence or independence depending on parameter values.We present some unique properties including mirroring the dependence structure in real data,dealing with the undesirable signature patterns found in most parametricmax-stable processes,and being directly applicable to real data.ABayesian inference approach is developed for the proposed model,and it is applied to simulated and real data.

LÉVY AREA ANALYSIS AND PARAMETER ESTIMATION FOR FOU PROCESSES VIA NON-GEOMETRIC ROUGH PATH THEORY

This paper addresses the estimation problem of an unknown drift parameter matrix for a fractional Ornstein-Uhlenbeck process in a multi-dimensional setting.To tackle this problem,we propose a novel approach based on rough path theory that allows us to construct pathwise rough path estimators from both continuous and discrete observations of a single path.Our approach is particularly suitable for high-frequency data.To formulate the parameter estimators,we introduce a theory of pathwise Itôintegrals with respect to fractional Brownian motion.By establishing the regularity of fractional Ornstein-Uhlenbeck processes and analyzing the long-term behavior of the associated Lévy area processes,we demonstrate that our estimators are strongly consistent and pathwise stable.Our findings offer a new perspective on estimating the drift parameter matrix for fractional Ornstein-Uhlenbeck processes in multi-dimensional settings,and may have practical implications for fields including finance,economics,and engineering.

2017—2022年中国农产品进出口贸易形势的变化与启示

2023年5月之前的一系列国内国际政策松绑意味着新冠肺炎疫情的全球形势已经从紧急状态向常态化转变。然而对整个新冠肺炎疫情期间中国农产品进出口贸易宏观数据异常变动的研究极度匮乏。采用新冠肺炎疫情前3年和整个疫情3年,即2017—2022年中国农产品进出口贸易宏观数据进行统计分析,结合文献综述、数据定量分析、对比研究等方法梳理、归纳和总结了新冠肺炎疫情期间中国农产品贸易进出口形势相对于疫情前形势的变化,深入分析疫情前和整个疫情过程中我国农产品进出口贸易的特征。结果表明,新冠肺炎疫情期间我国农产品进出口贸易总额呈持续增长态势;受世界多国农产品贸易限制、国内农产品生产价格指数上升和中美贸易协议的影响,中国农产品进出口贸易逆差进一步扩大。谷物、棉花、食糖等主要农产品进出口贸易的量价波动幅度较大。总体来看,疫情下我国农产品进出口贸易的基本结构未发生变化。新冠肺炎疫情没有改变我国农业自身资源的先天禀赋,也不改变我国农业结构、贸易环境和政府农业政策。2017—2022年中国农产品进出口贸易结构总体未发生变化,但是农产品贸易指标在2020年初体现出显著突发性,农产品贸易阶段性影响显著,少数农产品贸易结构性影响显著。建议大力确国家保粮食安全底线,提高疫情风险下中国在国际农产品贸易市场上的议价能力,持续发挥中国农产品贸易的比较优势。

M-estimation for Periodic GARCH Model with High-frequency Data

This paper studies an M-estimator of a proxy periodic GARCH （p, q） scaling model and establishes its consistency and asymptotic normality. Simulation studies are carried out to assess the performance of the estimator. The numerical results show that our M-estimator is more efficient and robust than other estimators without the use of high-frequency data.

按键精灵在福建省医疗保障系统的信息化应用

福建省医疗保障系统,是医保部门最重要的业务经办平台,需支撑各类医保数据的处理。文章以按键精灵的信息化应用为例,将医保部门的人工业务经办操作,如鼠标键盘的按键动作,用按键精灵的脚本录制或脚本编辑功能,以代码的形式信息化并制作成脚本。通过运行该脚本,进行人机按键模拟操作,实现医保数据的批量录入、批量复制和业务操作的自动化等功能,解决福建省医疗保障系统中业务经办出现的常见问题。

宏观统计数据质量规范研究

根据国内外宏观统计数据质量规范演变的历程,在界定宏观统计数据质量新内涵的基础上,依据国际货币基金组织评价数据质量的规范,本文对我国宏观数据质量的规范进行了实证研究。我国宏观数据质量在金融统计数据、社会人口统计数据上与国际货币基金组织制定的"数据公布通用系统"(GDDS)一致,而财政统计数据与对外经济数据与GDDS尚存在差距;我国数据公布系统在金融统计和人口统计上与GDDS一致,在对外统计与财政统计上与GDDS存在一定差异。