长三角区域旅游产业潜力研究

文章利用高等数学、经济学等的理论与方法,自主构建了基于最速下降法的区域旅游潜力研究分析方法,利用该分析方法对长三角区域旅游进行分析研究.首先采用曲线化的分析方法,对长三角区域旅游的发展趋势展开宏观性分析与研究.针对曲线分析深度不足的缺陷,展开模型化的实证分析.实证分析过程,构建了拓展的引力-斥力模型,使用该模型进行长三角区域旅游潜力分析.由此,找到了如何解决长三角区域旅游短板问题的手段和方法.同时对如何扩大长三角区域旅游潜力提出了整体对策,为该区域旅游产业稳定、深入发展提供了决策思路与依据.     

中国经济型酒店的冷与热——中国经济型酒店发展浅析

经济型酒店的概念产生于上世纪80年代的美国。二战以后，美国人的生活水平有了很大提高，人们有更多的时间和金钱用于度假、旅游，商务活动也开始日渐频繁，而高速公路网的建成为人们的出行提供更多的便利，“Holiday Inn”、“Super8”等经济型酒店随之应运而生。     

联合分析方法在医学领域中的应用

联合分析方法(Conjoint Analysis CA)是一种多元分析方法,是在数学心理学中发展起来的资料收集和分析的一种调查方法,具有坚实的理论基础。20世纪70年代,联合分析方法在市场营销的研究中产生,到了90年代,它已经在市场研究、交通经济学和环境经济学中得到成功的运用。近年来,联合分析方法在医学领域的应用主要包括研究病人和社区居民对卫生服务的偏好;研究健康咨询者的偏好;研制结果标准;给病人确定优化的治疗方案;在随机对照试验中对可供选择的方法进行     

空间分析支持下的土地资源信息的空间采样方法研究

1.引言60年代初期由Krige.D.G.和Matheron.G.创立并发展了地质统计学，其核心就是地质变量的空间插值方法-克里金法。该方法已在矿产储量的计算中得到广泛应用。此外，大量空间分析方法如趋势面分析也得到广泛应用。空间统计学的方法已逐步在地形制图、海底地形测量以及环境效应分析等不同的领域得到应用。上述研究工作主要针对空间分布的分析，而对涉及到空间分布随时间变化的过程分析尚待进一步的研究。我国在80年代和90年代前期的土地资源的研究特别是在资源调查中已开始采用采样技术提高调查的精度。如在第二次土壤普查的同时采用采样…     

模糊聚类分析方法在企业经济效益分类中的应用

在统计工作过程中，当找们取得了大量统计资料以后，只有通L科学的分组．才能分析研究社会经济现象中各种类型的持点，各个指标之间的相互关系，说明社会经济现象内部的结构及其在时间上的变化，发展趋势和发展规律．但在实际工作中，有的现象特征十分复杂，按照经典的统计分组方法难以分类和比较，对于这种十分复杂的各种模糊关系上的现象特征．我们使用模糊聚类的分析方法进行统计分组。下面就简单地介绍模糊聚类在企业经济效益中的应用。如下表（表1）是十个商业企业的有关经济效益统计资料：表1不难发现各企业在每项指标上的数值是一种…     

深圳股票市场成分指数的持续性分析

一、引言持续性分析(Duration Analysis)又称“生存分析”(Survival Analysis)或“风险性分析”(HazardAnalysis)。它是研究某一变量持续某种状态的时间长度的分析方法,已被广泛地应用于工程、医学和生物学等领域。70年代末,经济学家开始采用这种分析方法研究失业时间的持续性,     

旅游大数据研究热点及特征探析——基于国外文献的分析

大数据是近几年最热的词汇之一,它影响着当今社会的各行各业,旅游大数据的研究虽然出现时间较晚,但发展很快。选择Web of Science以及Google学术作为数据获取来源,截至2017年12月,收集到旅游大数据研究的有效文献91篇,对其进行分析后发现,研究热点主要有:从计算机科学视角研究旅游大数据的来源、获取和分析;从经济学视角研究旅游大数据对旅游知识生成和旅游消费行为的影响,运用旅游大数据研究旅游需求;从信息学视角运用旅游大数据研究智慧旅游;从地理学视角研究旅游交通和旅游流等。国外旅游大数据研究呈现议题的宽广性、工具和方法的多元化、应用的问题导向等特征,未来研究重点可以在研究方法和工具的进一步完善、研究内容的进一步拓展、旅游研究范式的构建等方面。     

旅游需求预测模型概述

旅游需求预测的研究工作始于20世纪60年代,所采用的方法有计量经济方法与定性研究方法.由于计量经济方法具有较强的说服力,所以大多数研究采取的是计量经济研究方法.     

数据挖掘中一种新的聚类方法——基于对应分析与因子旋转

内容提要：本文基于Q型因子分析的基本思想,结合对应分析方法,建立了一种适用于大型数据库聚类的方法。该方法既解决了Q型因子分析算法效率方面的问题,也解决了传统对应分析法中缺乏客观分类标准、信息损失严重等多种缺陷,在实证分析中也取得了良好的效果。     

加拿大旅游卫星账户的编制

上个世纪80年代开始,对旅游的经济贡献进行测算引起了各国的重视。一种新的统计方法——旅游卫星账户(TSA:TourismSatelliteAccount)应运而生。这种方法能够从花费、增加值以及就业等方面测算旅游业对经济的影响。2000年3月,联合国统计司、世界旅游组织、经济合作与发展组织及欧洲共同体统计处联合公布了《旅游卫星账户:推荐方法框架》,标志着旅游统计有了新的国际标准,而旅游统计也进入了新的发展阶段。加拿大在此领域内,做出了突出的贡献。它既是最早开展旅游卫星账户研究的国家,也是率先编制全国性和省级及地区级旅游卫星账户的国家。…     

基于“一般到简单”建模法的入境旅游需求研究

随着国际旅游业的迅速发展,入境旅游需求成为学术界研究的热点领域。本文基于1979-2008年中国4个主要客源国的数据,采用“一般到简单”建模法分析了入境旅游需求的主要影响因素及预测模型的选择。结果表明,旅游者的忠诚行为是影响入境旅游需求的最主要的因素;价格和收入对不同客源国的旅游需求的影响程度不同;各种经济社会及自然因素等不可控因素都大幅度影响国际旅游需求,使中国旅游业严重受挫;旅游需求的预测应基于经济理论和统计检验结果。     

Time-series forecasting with a novel fuzzy time-series approach: an example for Istanbul stock market

Artificial intelligence procedures such as artificial neural networks (ANNs), genetic algorithms and particle swarm optimization and other procedures such as fuzzy clustering have been successfully used in the various stages of different fuzzy time-series forecasting approaches. Fuzzy clustering, genetic algorithm and particle swarm optimization are generally used in the fuzzification stage, and this simplifies the applicability of this stage and makes the fuzzy time-series approach more systematic. ANNs have also been applied successfully in the fuzzy relationship determination stage. In this study, we propose a new hybrid fuzzy time-series approach in which fuzzy c-means clustering procedure is employed in the fuzzification stage and feed-forward neural networks are used in the fuzzy relationship determination stage. This study also includes an empirical analysis pertaining to the forecasting of Index 100 for the stocks and bonds exchange market of Istanbul.     

一种基于SIS的贝叶斯模型算法

贝叶斯动态模型及其预测理论具有广泛的应用性,如在通信,控制,人工智能,经济管理,气象预报等领域。本文简要介绍了贝叶斯动态模型,对于非线性贝叶斯动态模型提出了SIS算法及其在处理非线性模型中应用。     

Functional time series approach for forecasting very short-term electricity demand

This empirical paper presents a number of functional modelling and forecasting methods for predicting very short-term (such as minute-by-minute) electricity demand. The proposed functional methods slice a seasonal univariate time series (TS) into a TS of curves; reduce the dimensionality of curves by applying functional principal component analysis before using a univariate TS forecasting method and regression techniques. As data points in the daily electricity demand are sequentially observed, a forecast updating method can greatly improve the accuracy of point forecasts. Moreover, we present a non-parametric bootstrap approach to construct and update prediction intervals, and compare the point and interval forecast accuracy with some naive benchmark methods. The proposed methods are illustrated by the half-hourly electricity demand from Monday to Sunday in South Australia.     

Dynamic financial distress prediction based on Kalman filtering

In models for predicting financial distress, ranging from traditional statistical models to artificial intelligence models, scholars have primarily paid attention to improving predictive accuracy as well as the progressivism and intellectualization of the prognostic methods. However, the extant models use static or short-term data rather than time-series data to draw inferences on future financial distress. If financial distress occurs at the end of a progressive process, then omitting time series of historical financial ratios from the analysis ignores the cumulative effect of previous financial ratios on the current consequences. This study incorporated the cumulative characteristics of financial distress by using the characteristics of a state space model that is able to perform long-term forecasts to dynamically predict an enterprise's financial distress. Kalman filtering is used to estimate the model parameters. Thus, the model constructed in this paper is a dynamic financial prediction model that has the benefit of forecasting over the long term. Additionally, current data are used to forecast the future annual financial position and to judge whether the establishment will be in financial distress.     

无偏GM（1,1）幂模型及其应用

针对GM（1,1）幂模型灰微分方程与白化方程无法匹配的缺陷,以灰微分方程的重构为基础,建立无偏GM（1,1）幂模型。该方法使得差分方程的参数与其在微分方程中对应的参数具有更好的一致性。将无偏GM（1,1）幂模型应用到旅游客源预测中,实例应用结果显示无偏GM（1,1）幂模型预测精度高于GM（1,1）模型。     

新时代发挥统计监督职能的思考

党的十九届四中全会将统计监督纳入国家监督体系之中。随着中国特色社会主义进入新时代以及大数据与人工智能技术的快速发展与应用,统计监督面对的内外部环境都发生了深刻的变化,如何在新环境下进一步发挥统计监督作用成为了一个十分重要的问题。本文系统梳理了有关统计监督的各种论述,对统计监督的内涵进行了重新认识,回顾了新中国成立以来我国统计监督的实践和做法,并与国外统计监督进行了比较,最后提出了在新时代进一步发挥统计监督职能的若干建议。     

Forecasting interest rates: a comparative assessment of some second-generation nonlinear models

Modeling and forecasting of interest rates has traditionally proceeded in the framework of linear stationary methods such as ARMA and VAR, but only with moderate success. We examine here three methods, which account for several specific features of the real world asset prices such as nonstationarity and nonlinearity. Our three candidate methods are based, respectively, on a combined wavelet artificial neural network (WANN) analysis, a mixed spectrum (MS) analysis and nonlinear ARMA models with Fourier coefficients (FNLARMA). These models are applied to weekly data on interest rates in India and their forecasting performance is evaluated vis-à-vis three GARCH models [GARCH (1,1), GARCH-M (1,1) and EGARCH (1,1)] as well as the random walk model. Both the WANN and MS methods show marked improvement over other benchmark models, and may thus hold out several potentials for real world modeling and forecasting of financial data.     

Explaining and Forecasting Online Auction Prices and Their Dynamics Using Functional Data Analysis

Online auctions have become increasingly popular in recent years, and as a consequence there is a growing body of empirical research on this topic. Most of that research treats data from online auctions as cross-sectional, and consequently ignores the changing dynamics that occur during an auction. In this article we take a different look at online auctions and propose to study an auction's price evolution and associated price dynamics. Specifically, we develop a dynamic forecasting system to predict the price of an ongoing auction. By dynamic, we mean that the model can predict the price of an auction “in progress” and can update its prediction based on newly arriving information. Forecasting price in online auctions is challenging because traditional forecasting methods cannot adequately account for two features of online auction data: (1) the unequal spacing of bids and (2) the changing dynamics of price and bidding throughout the auction. Our dynamic forecasting model accounts for these special features by using modern functional data analysis techniques. Specifically, we estimate an auction's price velocity and acceleration and use these dynamics, together with other auction-related information, to develop a dynamic functional forecasting model. We also use the functional context to systematically describe the empirical regularities of auction dynamics. We apply our method to a novel set of Harry Potter and Microsoft Xbox data and show that our forecasting model outperforms traditional methods.     

门限自回归模型在经济分析和预测中的应用

The paper presents the application of Threshold Auto-regression ( TAR ) model in dealing with the nonlinearity in economy. With SETAR model, forecasting of 31 China’s macro-economic series is performed and the accuracy of different preprocessing methods are compared. With TARSC model, China’s Adjacent National Income Index and Accumulation rate are used to analyze economic fluctuation, empirical in terpretation of the fluctuation and leading and lagging are also presented. The results of the application are satisfactory.