A neural network based reputation bootstrapping approach for service selection

With the concept of service-oriented computing becoming widely accepted in enterprise application integration, more and more computing resources are encapsulated as services and published online. Reputation mechanism has been studied to establish trust on prior unknown services. One of the limitations of current reputation mechanisms is that they cannot assess the reputation of newly deployed services as no record of their previous behaviours exists. Most of the current bootstrapping approaches merely assign default reputation values to newcomers. However, by this kind of methods, either newcomers or existing services will be favoured. In this paper, we present a novel reputation bootstrapping approach, where correlations between features and performance of existing services are learned through an artificial neural network (ANN) and they are then generalised to establish a tentative reputation when evaluating new and unknown services. Reputations of services published previously by the same provider are also incorporated for reputation bootstrapping if available. The proposed reputation bootstrapping approach is seamlessly embedded into an existing reputation model and implemented in the extended service-oriented architecture. Empirical studies of the proposed approach are shown at last.     

The construction and estimation of continuous time models and discrete approximations in econometrics

We consider econometric models involving variables that are defined continuously over time, or more frequently than they are observed. Separate but analogous treatment is given to both closed models (involving no exogenous variables) and open models (involvingexogenous variables). Justification for the use of standard discrete time models is given. Some exact discrete time models, and some computationally convenient approximate ones, are considered. Asymptotically efficient estimation procedures for a wide class of models are described.     

The tourism forecasting competition

We evaluate the performances of various methods for forecasting tourism data. The data used include 366 monthly series, 427 quarterly series and 518 annual series, all supplied to us by either tourism bodies or academics who had used them in previous tourism forecasting studies. The forecasting methods implemented in the competition are univariate and multivariate time series approaches, and econometric models. This forecasting competition differs from previous competitions in several ways: (i) we concentrate on tourism data only; (ii) we include approaches with explanatory variables; (iii) we evaluate the forecast interval coverage as well as the point forecast accuracy; (iv) we observe the effect of temporal aggregation on the forecasting accuracy; and (v) we consider the mean absolute scaled error as an alternative forecasting accuracy measure. We find that pure time series approaches provide more accurate forecasts for tourism data than models with explanatory variables. For seasonal data we implement three fully automated pure time series algorithms that generate accurate point forecasts, and two of these also produce forecast coverage probabilities which are satisfactorily close to the nominal rates. For annual data we find that Naïve forecasts are hard to beat.     

Composite likelihood methods for large Bayesian VARs with stochastic volatility

Adding multivariate stochastic volatility of a flexible form to large vector autoregressions (VARs) involving over 100 variables has proved challenging owing to computational considerations and overparametrization concerns. The existing literature works with either homoskedastic models or smaller models with restrictive forms for the stochastic volatility. In this paper, we develop composite likelihood methods for large VARs with multivariate stochastic volatility. These involve estimating large numbers of parsimonious models and then taking a weighted average across these models. We discuss various schemes for choosing the weights. In our empirical work involving VARs of up to 196 variables, we show that composite likelihood methods forecast much better than the most popular large VAR approach, which is computationally practical in very high dimensions: the homoskedastic VAR with Minnesota prior. We also compare our methods to various popular approaches that allow for stochastic volatility using medium and small VARs involving up to 20 variables. We find our methods to forecast appreciably better than these as well.     

An empirical analysis of neural network memory structures for basin water quality forecasting

This research investigates the cumulative multi-period forecast accuracy of a diverse set of potential forecasting models for basin water quality management. The models are characterized by their short-term (memory by delay or memory by feedback) and long-term (linear or nonlinear) memory structures. The experiments are conducted as a series of forecast cycles, with a rolling origin of a constant fit size. The models are recalibrated with each cycle, and out-of-sample forecasts are generated for a five-period forecast horizon. The results confirm that the JENN and GMNN neural network models are generally more accurate than competitors for cumulative multi-period basin water quality prediction. For example, the JENN and GMNN models reduce the cumulative five-period forecast errors by as much as 50%, relative to exponential smoothing and ARIMA models. These findings are significant in view of the increasing social and economic consequences of basin water quality management, and have the potential for extention to other scientific, medical, and business applications where multi-period predictions of nonlinear time series are critical.     

Co-ordination and integration mechanisms to manage logistics processes across supply networks

Literature on supply chain management (SCM) emphasises the importance of co-ordination and integration mechanisms to manage logistics processes successfully across supply networks. This requires managers to (1) know the driver variables that must be addressed, since they determine how such processes can be designed and managed; and (2) understand how co-ordination and integration mechanisms interact with such variables and—as a consequence—with logistics processes. The paper addresses the second issue, as it tries to explain how logistics processes can be structured and controlled across supply networks by leveraging co-ordination and integration mechanisms, with consequences for strategic and operational choices for both the individual companies and the whole supply network. This issue has been investigated by analysing three case-studies of SCM interventions on logistics processes across different supply networks, involving central firms as well as several suppliers and customers.     

Design and development of inventory knowledge discovery system

Inventory management (IM) performance is affected by the forecasting accuracy of both demand and supply. In this paper, an inventory knowledge discovery system (IKDS) is designed and developed to forecast and acquire knowledge among variables for demand forecasting. In IKDS, the TREes PArroting Networks (TREPAN) algorithm is used to extract knowledge from trained networks in the form of decision trees which can be used to understand previously unknown relationships between the input variables so as to improve the forecasting performance for IM. The experimental results show that the forecasting accuracy using TREPAN is superior to traditional methods like moving average and autoregressive integrated moving average. In addition, the knowledge extracted from IKDS is represented in a comprehensible way and can be used to facilitate human decision-making.     

U-Convolutional model for spatio-temporal wind speed forecasting

The increasing penetration of intermittent renewable energy in power systems brings operational challenges. One way of supporting them is by enhancing the predictability of renewables through accurate forecasting. Convolutional Neural Networks (Convnets) provide a successful technique for processing space-structured multi-dimensional data. In our work, we propose the U-Convolutional model to predict hourly wind speeds for a single location using spatio-temporal data with multiple explanatory variables as an input. The U-Convolutional model is composed of a U-Net part, which synthesizes input information, and a Convnet part, which maps the synthesized data into a single-site wind prediction. We compare our approach with advanced Convnets, a fully connected neural network, and univariate models. We use time series from the Climate Forecast System Reanalysis as datasets and select temperature and u- and v-components of wind as explanatory variables. The proposed models are evaluated at multiple locations (totaling 181 target series) and multiple forecasting horizons. The results indicate that our proposal is promising for spatio-temporal wind speed prediction, with results that show competitive performance on both time horizons for all datasets.     

基于BP神经网络实现的B样条插值在系统动力学模型的应用

在系统动力学建模的过程中,系统变量之间的关系难以确定,传统的建模方法存在着很大的主观性,利用BP神经网络的方法则可以避免这个问题。而在建立BP神经网络过程中需要大量的学习样本,然而通常采集到的数据往往是不足的。用3次B样条函数对历史数据进行插值的方法构建系统中状态变量的学习样本,解决BP神经网络模型中学习样本不足的缺点,能更好的反应变量之间的非线性映射关系。     

BP神经网络在项目投资风险分析中的应用

影响项目投资经济效益评价指标的参数很多,这些参数均具有一定的不确定性.由于各参数的不确定性,其项目投资经济效益评价指标值也是不确定的,因此,对投资项目所做的经济效益评价结论必将带有风险.在经济评价工作中,应当对这种风险作出合理的量化分析和估算,以了解项目的风险承受能力,并尽可能采取措施回避风险.本文共搜集了10个相似投资项目,根据BP神经网络工作原理筛选确定了6个影响和制约项目投资因素的指标作为神经网络的输入变量,并提出了基于BP神经网络的项目投资风险分析模型;其中7个投资项目作为训练样本,2个作为检测实例,最后一个进行敏感性分析.经过测算,其精度完全可以满足实际项目投资风险分析的需要.因此,神经网络在这方面有很好的应用前景.     

基于主成分分析-BP神经网络的道路客运站场布局决策研究

为解决输入变量过多所造成的BP神经网络系统效率下降问题,提出一种主成分分析-BP神经网络的道路客运站场布局决策方法。首先,利用主成分分析方法,将个数较多的原始输入变量群变换为一组个数较少且彼此独立的新输入变量;然后,将新的输入变量群作为BP神经网络的输入进行道路客运站场的布局决策;最后,以廊坊市道路客运站场布局为例验证了方法的有效性。     

Extend (r, Q) Inventory Model Under Lead Time and Ordering Cost Reductions When the Receiving Quantity is Different from the Ordered Quantity

This paper investigates the continuous review inventory model involving variable lead time with partial backorders, where the amount received is uncertain. The options of investing in ordering cost reduction is included, and lead time can be shortened at an extra crashing cost. The objective of this article is to simultaneously optimize the order quantity, reorder point, ordering cost and lead time. We first assume that the lead time demand follows a normal distribution and develop an algorithm to find the optimal solution. Then, we relax the assumption of normality to consider a distribution free case where only the mean and standard deviation of lead time demand are known. We apply the minimax distribution free procedure to solve this problem. For both cases, we also show that the objective cost function to be minimized is jointly convex in the decision variables. Furthermore, two numerical examples are given to illustrate the results.     

基于集成学习的多式联运货运量预测模型构建

货运量精准预测是多式联运网络高效协同发展的重要基础,货运量时变性强、数据多样性缺失是实现精准货运量预测的问题所在。基于此,通过挖掘货物运输量(集装箱)的时间变化特征,构建初始相关时间特征输入集,结合斯皮尔曼相关性系数分布,采用Bagging+BP集成学习方法训练多个弱分类器,最终组合获取高精度的强学习模型。以南京龙潭港为例,对自回归移动平均模型(ARIMA)、Bagging+BP集成学习网络以及长短时记忆神经网络(LSTM)三种模型进行评价,实验结果表明,相比于其他模型,提出的Bagging+BP集成学习网络预测性能良好,有一定的实用价值。     

Towards a flexible online mixed method design with a feedback loop

The notion of mixed methods design relates to the research studies that combine qualitative and quantitative approaches. However, most of these studies are tailored to specific research problem in a particular study and are typically limited to a fixed sequence of qualitative and quantitative approaches (e.g. qualitative interviews followed by a survey or vice-versa). This limitation historically arises from time, cost and logistic restrictions. As an alternative, we develop an general extension of fixed mixed method design by introducing a flexible feedback-loop so that several phases can be combined in a flexible order. In practice, such designs are now increasingly feasible within an information-communication technology environment, where online respondents are readily available for immediate participation. An online experiment combining interactive series of web surveys and in-depth web interviews was performed to compare this approach with standard two-phase designs involving mixed methods. The costs, timing, quality of finiding and experiences of researchers were systematically evaluated. In summary, the proposed approach proved to be very beneficial.     

Bridging the Gap between Different Statistical Approaches: An Integrated Framework for Modelling

This paper proposes a template for modelling complex datasets that integrates traditional statistical modelling approaches with more recent advances in statistics and modelling through an exploratory framework. Our approach builds on the well-known and long standing traditional idea of 'good practice in statistics' by establishing a comprehensive framework for modelling that focuses on exploration, prediction, interpretation and reliability assessment, a relatively new idea that allows individual assessment of predictions.
The integrated framework we present comprises two stages. The first involves the use of exploratory methods to help visually understand the data and identify a parsimonious set of explanatory variables. The second encompasses a two step modelling process, where the use of non-parametric methods such as decision trees and generalized additive models are promoted to identify important variables and their modelling relationship with the response before a final predictive model is considered. We focus on fitting the predictive model using parametric, non-parametric and Bayesian approaches.
This paper is motivated by a medical problem where interest focuses on developing a risk stratification system for morbidity of 1,710 cardiac patients given a suite of demographic, clinical and preoperative variables. Although the methods we use are applied specifically to this case study, these methods can be applied across any field, irrespective of the type of response.     

BP神经网络在WSN数据融合中的应用

在无线传感器网络(WSNs)的研究与应用中,利用数据融合来提高网络中能量利用率是其中一个重要的研究方向。文章利用BP(Back Propagation)神经网络能够对曲线进行无限逼近的特性来对无线传感器的监测数据进行数据拟合,然后传输拟合好的权值与阈值,同时通过将上一次拟合的权值与阈值赋予下一次拟合来减少神经网络的训练步数。模拟实验表明利用该方案能够有效减少数据的传输量,从而达到高效利用传感器能量的目的。     

Valuing unlisted shares: A dual approach to the major information determinants

Little is known about how professional valuation experts actually form judgements on the value of unlisted shares. This study examines the valuation process among Canadian valuators and the relative importance of each of the main information variables used in that process. A dual approach to the problem is adopted: (1) a major survey of 231 valuation experts and (2) a conjoint analysis experiment on 82 valuators using fabricated cases representative of realistic relationships. Both approaches conclude that while earnings prospects is the single most important factor in determining unlisted share values, the determination of value in the absence of a capital market is a highly complex process involving a host of information variables, many of which do not easily lend themselves to objective judgement.     

线性振动的神经网络建模

本文利用人工神经网络对两自由度线性振动系统进行了神经网络建模,并通过所建立的神经网络模型对该系统进行了预测。分别利用MATLAB和BP网络作为平台和训练工具。以两自由度悬臂梁的受迫振动为例,将一段时间内的激励力作为网络的输入参数,对应于该段时间内由振动产生的挠度作为网络的输出参数,然后利用BP网络进行训练。将网络模型预测结果与精确解进行对比,误差甚小。该结果表明:所建立的神经网络模型合理、有效,可利用其对该类问题进行预测并应用于工程实践中。     

The Ordinal Controversy Revisited

We present a discussion of the different dimensions of the ongoing controversy about the analysis of ordinal variables. The source of this controversy is traced to the earliest possible stage, measurement theory. Three major approaches in analyzing ordinal variables, called the non-parametric, the parametric, and the underlying variable approach, are identified and the merits and drawbacks of each of these approaches are pointed out. We show that the controversy on the exact definition of an ordinal variable causes problems with regard to defining ordinal association, and therefore to the interpretation of many recently designed models for ordinal variables, e.g., structure equation models using polychoric correlations, latent class models and ordinal response models. We conclude that the discussion with regard to ordinal variable modeling can only be fruitful if one makes a distinction between different types of ordinal variables. Five types of ordinal variables were identified. The problems concerning the analysis of these five types of ordinal variables are solved in some cases and remain a problem for others.     

Nonparametric least squares methods for stochastic frontier models

When analyzing productivity and efficiency of firms, stochastic frontier models are very attractive because they allow, as in typical regression models, to introduce some noise in the Data Generating Process . Most of the approaches so far have been using very restrictive fully parametric specified models, both for the frontier function and for the components of the stochastic terms. Recently, local MLE approaches were introduced to relax these parametric hypotheses. In this work we show that most of the benefits of the local MLE approach can be obtained with less assumptions and involving much easier, faster and numerically more robust computations, by using nonparametric least-squares methods. Our approach can also be viewed as a semi-parametric generalization of the so-called “modified OLS” that was introduced in the parametric setup. If the final evaluation of individual efficiencies requires, as in the local MLE approach, the local specification of the distributions of noise and inefficiencies, it is shown that a lot can be learned on the production process without such specifications. Even elasticities of the mean inefficiency can be analyzed with unspecified noise distribution and a general class of local one-parameter scale family for inefficiencies. This allows to discuss the variation in inefficiency levels with respect to explanatory variables with minimal assumptions on the Data Generating Process.