B2B电子商务模式的探讨

针对B2B模式将成为未来电子商务发展的主流,文章先从价值链角度分析了B2B电子商务模式对价值链上游的冲击,然后概括性地总结了目前主要的3种B2B电子商务模式,最后用“SWOT”战略评估方法分析了基于供应链管理的区域型B2B电子商务新模式。     

房地产企业商务模式创新分析

商务模式是企业界非常流行的术语，商务模式创新正在成为各国企业竞争的一个重要领域。本文通过运用厦门大学翁君奕教授的商务模式创新理论，从价值主张、价值支撑和价值保持三个层面对名城地产商务模式进行分析，并得出结论：创新的商务模式有利于企业获取成功。     

移动商务的价值链与商务模式研究

随着网络技术的不断进步，移动商务也渐渐成为一种新型的沟通方式，代表着网络时代商务模式发展的方向。本文从价值链和商务模式的角度探讨了移动商务的各种运作模式和利润实现模式，以期对中国移动商务的发展起到实际指导作用。     

基于创新空间的移动商务经营模式策略研究

经营模式创新首先要解决的是方向性问题,这是保证企业新经营模式实施成功的重要保障。本文从技术、组织和市场三个维度构建了移动商务经营模式的创新空间和评价指标体系,在此基础之上提出了移动商务经营模式创新策略选择的系统框架。将移动商务经营模式创新策略划分为三种情况:连续性创新与不连续性创新策略、延续性创新与破坏性创新策略、现有模式和新商业模式策略,并以移动价值链的核心成员为例,分析了各成员可采取的各种经营策略,意在为企业移动商务经营模式策略的选择提供依据。     

移动电子商务的商务模式

移动电子商务是在互联网电子商务的基础上发展而来的新的商务形式.本文首先介绍了移动电子商务的概念和特点,然后讨论了移动电子商务的商务模式,最后探讨了移动电子商务成功的关键因素.     

互联网驾培平台的电子商务模式研究

近些年,互联网驾培服务异军突起,很多驾培平台雨后春笋般地涌现,但是有关互联网驾培平台的研究很少。本文的研究不但可以丰富这个方面的文献,而且可以为驾培领域的经营者和创业者提供该领域的商务模式和发展趋势。本文首先阐述了互联网驾培平台的概念,其次分析了互联网驾培平台的基本情况,再次重点研究了互联网驾培平台的商业模式、经营模式和管理模式,最后提出了互联网驾培平台的成功因素、存在的问题和改进建议。     

中小企业电子商务网站运营模式的创新策略探讨

随着互联网技术和电子商务的快速发展,中小企业电子商务已经成为未来企业发展的重要方向。文章通过对我国现阶段中小企业电子商务网站运营过程中存在的实际问题展开分析,并结合实际情况提出优化中小企业电子商务网站运营的基本策略,从创新的角度分析优化未来网站运营模式的具体路径,旨在为全面提升中小企业电子商务网站运营的竞争力提供参考。     

新的商务模式下ERP系统在石油钻井企业的应用

随着通信技术、计算机技术及先进管理思想的不断发展和广泛应用,电子商务、移动商务和协同商务等商务模式相继出现,传统ERP系统已不能满足企业现有经营管理需求。在新的商务模式下钻井ERP应以企业自身生产经营活动为中心,与企业生产执行系统MES、决策支持系统DSS、供应链管理SCM、客户关系管理CRM、价值链管理VM、知识管理KM、(移动)电子商务、商务智能B I等系统集成,实现与供应商、油田公司、上级部门等外部信息共享,扩展其应用功能,达到企业内外部协同工作,满足企业当前及未来发展需求。     

TAFE模式下国际商务管理专业教学探讨

本文通过对澳大利亚的TAFE模式的研究,分析当前我国国际商务管理专业教学中存在的问题,提出商务管理专业应突出能力培养的特点,重点在于师资建设和在教学过程中实现对学生技能的培养,以满足社会需求。     

移动商务的模式架构和发展对策

本文从移动商务的定义、特点出发,分析移动商务市场情况。架构移动商务的发展模式,提出移动商务的发展对策。     

LoMEF: A framework to produce local explanations for global model time series forecasts

Global forecasting models (GFMs) that are trained across a set of multiple time series have shown superior results in many forecasting competitions and real-world applications compared with univariate forecasting approaches. One aspect of the popularity of statistical forecasting models such as ETS and ARIMA is their relative simplicity and interpretability (in terms of relevant lags, trend, seasonality, and other attributes), while GFMs typically lack interpretability, especially relating to particular time series. This reduces the trust and confidence of stakeholders when making decisions based on the forecasts without being able to understand the predictions. To mitigate this problem, we propose a novel local model-agnostic interpretability approach to explain the forecasts from GFMs. We train simpler univariate surrogate models that are considered interpretable (e.g., ETS) on the predictions of the GFM on samples within a neighbourhood that we obtain through bootstrapping, or straightforwardly as the one-step-ahead global black-box model forecasts of the time series which needs to be explained. After, we evaluate the explanations for the forecasts of the global models in both qualitative and quantitative aspects such as accuracy, fidelity, stability, and comprehensibility, and are able to show the benefits of our approach.     

Convergence rates of recombining trees for pricing options on stocks under GBM and regime-switching models with known cash dividends

In the literature there appear various kinds of binomial trees for pricing options on stocks under geometric Brownian motions (GBMs) with known cash dividends. The aim of this paper is to compare the performance of the existing binomial trees in aspect of the convergence rates, which are usually used to measure precisely how fast the approximate values converge to the exact one, and to give a theoretical proof of the convergence rates for the interpolation binomial trees which are based on a model that excludes the arbitrage possibilities. Also the paper extends the studies to the regime-switching models with known cash dividend payment.     

FACTOR MODELS AND TIME‐VARYING PARAMETER FRAMEWORK FOR FORECASTING EXCHANGE RATES AND INFLATION: A SURVEY

A survey of models used for forecasting exchange rates and inflation reveals that the factor‐based and time‐varying parameter or state space models generate superior forecasts relative to all other models. This survey also finds that models based on Taylor rule and portfolio balance theory have moderate predictive power for forecasting exchange rates. The evidence on the use of Bayesian Model Averaging approach in forecasting exchange rates reveals limited predictive power, but strong support for forecasting inflation. Overall, the evidence overwhelmingly points to the context of the forecasts, relevance of the historical data, data transformation, choice of the benchmark, selected time horizons, sample period and forecast evaluation methods as the crucial elements in selecting forecasting models for exchange rate and inflation.     

A comprehensive evaluation of macroeconomic forecasting methods

We employ datasets for seven developed economies and consider four classes of multivariate forecasting models in order to extend and enhance the empirical evidence in the macroeconomic forecasting literature. The evaluation considers forecasting horizons of between one quarter and two years ahead. We find that the structural model, a medium-sized DSGE model, provides accurate long-horizon US and UK inflation forecasts. We strike a balance between being comprehensive and producing clear messages by applying meta-analysis regressions to 2,976 relative accuracy comparisons that vary with the forecasting horizon, country, model class and specification, number of predictors, and evaluation period. For point and density forecasting of GDP growth and inflation, we find that models with large numbers of predictors do not outperform models with 13–14 hand-picked predictors. Factor-augmented models and equal-weighted combinations of single-predictor mixed-data sampling regressions are a better choice for dealing with large numbers of predictors than Bayesian VARs.     

Analysis and short-term predictions of non-technical loss of electric power based on mixed effects models

In this paper we estimate, analyze and predict short-term non-technical loss (NTL) of electric power of Brazilian energy service companies based on different assumptions for the covariance structure of the errors and controlling for socio-economic confounding variables. Although the correlation among repeated responses is not usually of intrinsic interest, it is an important aspect of the data that must properly be accounted for to produce valid inferences in longitudinal or panel data analysis. In the extended linear mixed effects model, the covariance matrix of the response vector is comprised by two subcomponents, a random effect component that can represent between group variation and a intraclass or within group component. So, in order to adequately treat the longitudinal character of NTL data, we use the decomposition of these variance components to evaluate different architectures to the within group errors. Using data of 59 Brazilian distributing utilities from 2004 to 2012, we fit a conditionally independent errors model and three other models with autoregressive-moving average parametrization to the intraclass disturbances. Finally, we compare models using the MAD and MAPE metrics in the prediction of NTL for the year of 2013. The findings suggest that the approach can be satisfactorily implemented in future statistical analysis of NTL.     

Hierarchical models for independence structures of networks

We introduce a new family of network models, called hierarchical network models, that allow us to represent in an explicit manner the stochastic dependence among the dyads (random ties) of the network. In particular, each member of this family can be associated with a graphical model defining conditional independence clauses among the dyads of the network, called the dependency graph. Every network model with dyadic independence assumption can be generalized to construct members of this new family. Using this new framework, we generalize the Erdös–Rényi and the β models to create hierarchical Erdös–Rényi and β models. We describe various methods for parameter estimation, as well as simulation studies for models with sparse dependency graphs.     

Geometrical Interpretation of the Mean and the Constant in a Box-Jenkins Time Series Model

Certain manuals and computer programs mistakenly identify the mean with the constant in Box-Jenkins time series models. In this paper, it will be shown that (a) the mean and the constant have different values in autoregressive models, and (b) they have an algebraic and graphical relationship.     

Comparing forecasts from fixed and variable coefficient models: The case of money demand

In this paper we introduce a class of tentatively plausible, fixed-coefficient models of money demand and evaluate their forecast performance. When these models are reestimated allowing all coefficients to vary over time, the forecasting performance improves dramatically. Aside from offering insights about improved methods of analyzing time series data, the most promising direct use for point estimates derived from time-varying coefficients is as an aid in calibrating proposed models of the kind discussed here.     

Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition

In this work we introduce the forecasting model with which we participated in the NN5 forecasting competition (the forecasting of 111 time series representing daily cash withdrawal amounts at ATM machines). The main idea of this model is to utilize the concept of forecast combination, which has proven to be an effective methodology in the forecasting literature. In the proposed system we attempted to follow a principled approach, and make use of some of the guidelines and concepts that are known in the forecasting literature to lead to superior performance. For example, we considered various previous comparison studies and time series competitions as guidance in determining which individual forecasting models to test (for possible inclusion in the forecast combination system). The final model ended up consisting of neural networks, Gaussian process regression, and linear models, combined by simple average. We also paid extra attention to the seasonality aspect, decomposing the seasonality into weekly (which is the strongest one), day of the month, and month of the year seasonality.     

Description of purchase incidence by multivariate heterogeneous Poisson processes

In this paper the application of bivariate Poisson heterogeneous models to budget data is studied. This study was motivated from inconsistencies that we encountered when univariate Poisson based models were applied to cumulative data sets. Application of a multivariate Poisson based model is a possible solution to this problem. In this paper we will study the feasibility of estimators based on these models.