模糊处理变结构神经网络日负荷预测方法研究

对于受不确定因素影响的日电力负荷,首次提出了基于模糊分类规则的变结构神经网络负荷预测模型,考虑从两方面改进预测精度,一个方面是通过模糊分类规则,使过去的负荷数据分为不同气候特征,选用同类特征数据进行预测,另一方面是通过神经网络变结构优化,确定最优网络和最优拟合逼近,从而得到最优的预测结果,这种新方法同时考虑了天气因素的影响和神经网络的最优确定,因此,较大提高了日负荷预测的精度。     

Incorporating demand uncertainty and forecast error in supply chain planning models

This paper develops a framework for examining the effect of demand uncertainty and forecast error on unit costs and customer service levels in the supply chain, including Material Requirements Planning (MRP) type manufacturing systems. The aim is to overcome the methodological limitations and confusion that has arisen in much earlier research. To illustrate the issues, the problem of estimating the value of improving forecasting accuracy for a manufacturer was simulated. The topic is of practical importance because manufacturers spend large sums of money in purchasing and staffing forecasting support systems to achieve more accurate forecasts. In order to estimate the value a two-level MRP system with lot sizing where the product is manufactured for stock was simulated. Final product demand was generated by two commonly occurring stochastic processes and with different variances. Different levels of forecasting error were then introduced to arrive at corresponding values for improving forecasting accuracy. The quantitative estimates of improved accuracy were found to depend on both the demand generating process and the forecasting method. Within this more complete framework, the substantive results confirm earlier research that the best lot sizing rules for the deterministic situation are the worst whenever there is uncertainty in demand. However, size matters, both in the demand uncertainty and forecasting errors. The quantitative differences depend on service level and also the form of demand uncertainty. Unit costs for a given service level increase exponentially as the uncertainty in the demand data increases. The paper also estimates the effects of mis-specification of different sizes of forecast error in addition to demand uncertainty. In those manufacturing problems with high demand uncertainty and high forecast error, improved forecast accuracy should lead to substantial percentage improvements in unit costs. Methodologically, the results demonstrate the need to simulate demand uncertainty and the forecasting process separately.     

扩展线性支出系统预测精度的验证研究

本文用我国城镇居民 1992— 1998年消费数据分别建立各年度的扩展线性支出系统模型 ,用模型预测值与实际发生值进行对比 ,从而检验扩展线性支出系统的预测精度。检验结果表明 ,预测误差随被预测年的推移而迅速增大。并对误差产生的原因作了分析。最后得出结论 :扩展线性支出系统一般只宜用作消费需求的短期预测。     

Tactical sales forecasting using a very large set of macroeconomic indicators

Tactical forecasting in supply chain management supports planning for inventory, scheduling production, and raw material purchase, amongst other functions. It typically refers to forecasts up to 12 months ahead. Traditional forecasting models take into account univariate information extrapolating from the past, but cannot anticipate macroeconomic events, such as steep increases or declines in national economic activity. In practice this is countered by using managerial expert judgement, which is well known to suffer from various biases, is expensive and not scalable. This paper evaluates multiple approaches to improve tactical sales forecasting using macro-economic leading indicators. The proposed statistical forecast selects automatically both the type of leading indicators, as well as the order of the lead for each of the selected indicators. However as the future values of the leading indicators are unknown an additional uncertainty is introduced. This uncertainty is controlled in our methodology by restricting inputs to an unconditional forecasting setup. We compare this with the conditional setup, where future indicator values are assumed to be known and assess the theoretical loss of forecast accuracy. We also evaluate purely statistical model building against judgement aided models, where potential leading indicators are pre-filtered by experts, quantifying the accuracy-cost trade-off. The proposed framework improves on forecasting accuracy over established time series benchmarks, while providing useful insights about the key leading indicators. We evaluate the proposed approach on a real case study and find 18.8% accuracy gains over the current forecasting process.     

Newsvendor models for innovative products with one-shot decision theory

In this paper, newsvendor problems for innovative products are analyzed. Because the product is new, no relevant historical data is available for statistical demand analysis. Instead of using the probability distribution, the possibility distribution is utilized to characterize the uncertainty of the demand. We consider products whose life cycles are expected to be smaller than the procurement lead times. Determining optimal order quantities of such products is a typical one-shot decision problem for a retailer. Therefore, newsvendor models for innovative products are proposed based on the one-shot decision theory (OSDT). The main contributions of this research are as follows: the general solutions of active, passive, apprehensive and daring focus points and optimal alternatives are proposed and the existence theorem is established in the one-shot decision theory; a simple and effective approach for identifying the possibility distribution is developed; newsvendor models with four types of focus points are built; managerial insights into the behaviors of different types of retailers are gained by the theoretical analysis; the proposed models are scenario-based decision models which provide a fundamental alternative to analyze newsvendor problems for innovative products.     

基于EMD-GA-BP与EMD-PSO-LSSVM的中国碳市场价格预测

由于碳交易市场价格的波动性大及相互影响关系的复杂性,本文试图构建碳价格长期和短期的最优预测模型。考虑到碳交易价格波动的趋势性和周期性特点,基于经验模态分解算法(EMD)、遗传算法(GA)—神经网络(BP)模型、粒子群算法(PSO)—最小二乘支持向量机(LSSVM)模型及由它们构建的组合预测模型,对中国碳市场交易价格进行短期预测和长期预测。实证分析中将影响碳交易价格的不同宏观经济因素和碳价格时间序列因素做为输入变量,分别代入组合模型进行预测。研究结果表明,在短期预测中,EMD-GA-BP模型预测效果优于GA-BP模型和PSO-LSSVM模型;而在长期预测中,组合模型EMD-PSO-LSSVM模型预测效果优于只考虑碳价格波动趋势性或周期性预测效果。     

Newsvendor-type models with decision-dependent uncertainty

Models for decision-making under uncertainty use probability distributions to represent variables whose values are unknown when the decisions are to be made. Often the distributions are estimated with observed data. Sometimes these variables depend on the decisions but the dependence is ignored in the decision maker??s model, that is, the decision maker models these variables as having an exogenous probability distribution independent of the decisions, whereas the probability distribution of the variables actually depend on the decisions. It has been shown in the context of revenue management problems that such modeling error can lead to systematic deterioration of decisions as the decision maker attempts to refine the estimates with observed data. Many questions remain to be addressed. Motivated by the revenue management, newsvendor, and a number of other problems, we consider a setting in which the optimal decision for the decision maker??s model is given by a particular quantile of the estimated distribution, and the empirical distribution is used as estimator. We give conditions under which the estimation and control process converges, and show that although in the limit the decision maker??s model appears to be consistent with the observed data, the modeling error can cause the limit decisions to be arbitrarily bad.     

Exchange rate uncertainty and trade growth—a comparison of linear and non‐linear (forecasting) models

A huge body of empirical and theoretical literature has emerged on the relationship between foreign exchange (FX) uncertainty and international trade. Empirical findings about the impact of FX uncertainty on trade figures are at best weak and often ambiguous with respect to its direction. Almost all empirical contributions assume and estimate a linear relationship. Possible nonlinearity or state dependence of causal links between FX uncertainty and trade has been mostly ignored yet. In addition, widely used regression models have not been evaluated in terms of ex‐ante forecasting. In this paper we analyse the impact of FX uncertainty on sectoral categories of multilateral exports and imports for 15 industrialized economies. We particularly provide a comparison of linear and non‐linear models with respect to ex‐ante forecasting. In terms of average ranks of absolute forecast errors non‐linear models outperform both, a common linear model and some specification building on the assumption that FX uncertainty and trade growth are uncorrelated. Our results support the view that the relationship of interest might be non‐linear and, moreover, lacks of homogeneity across countries, economic sectors and when contrasting imports vs exports. Copyright © 2005 John Wiley & Sons, Ltd.     

An existence and uniqueness theorem for an optimal inventory problem with forecasting

An existence and uniqueness theorem is proved for an optimal inventory problem with forecasting. The model assumes costs are fixed and that unsatisfied demand is lost. At each stage a forecast is obtained on the basis of which the decisionmaker has a known conditional probability distribution of demand. The theorem is a generalization of a result stated but not proved by White.     

基于修正的时间序列模型对冲击负荷的预测

电力负荷预测的实质是对电力市场需求的预测,是利用以往的历史数据资料找出电力负荷的变化规律,进而预测负荷在未来时期的变化趋势.由于经济、气候以及工业生产等诸多因素的约束和限制,电力负荷预测精度很难提高.一个好的实用的电力负荷预测模型则要求既能充分利用负荷的历史数据,又能灵活方便地综合考虑其他多种相关因素的影响.提出了回归与自回归模型相结合的时间序列混合回归预测模型,它的待估参数由BP神经网络进行修正,经实例验证,预测效果良好.     

Temperature prediction at critical points in district heating systems

Current methodologies for the optimal operation of district heating systems use model predictive control. Accurate forecasting of the water temperature at critical points is crucial for meeting constraints related to consumers while minimizing the production costs for the heat supplier. A new forecasting methodology based on conditional finite impulse response (cFIR) models is introduced, for which model coefficients are replaced by coefficient functions of the water flux at the supply point and of the time of day, allowing for nonlinear variations of the time delays. Appropriate estimation methods for both are described. Results are given for the test case of the Roskilde district heating system over a period of more than 6 years. The advantages of the proposed forecasting methodology in terms of a higher forecast accuracy, its use for simulation purposes, or alternatively for better understanding transfer functions of district heating systems, are clearly shown.     

基于模糊时间序列的传感器网络感知数据预测模型

传感器网络监控系统属于大型复杂系统,由感知节点以一定的时间间隔向sink节点发送感知数据,以实现对应用环境的监控。由于网络本身及应用环境的影响,得到的感知数据往往存在不确定性。此外,周期性报告数据模式影响到实时监控数据的精确性。本文应用时间序列模型预测传感器数据以响应用户查询,可有效降低网络通信量。通过对无线传感器网络的数据分析,引入多属性模糊时间序列预测模型,充分考虑了无线传感器网络时间序列中存在的趋势因素,并提出了适合于传感器网络的修正预测模型。实验结果表明模糊时间序列模型可有效预测传感器网络数据,且能提高预测精度。     

模糊时间数列的分析与预测:以台湾地区加权股价指数为例

随着我国经济快速成长，衍生性金融商品的投资分析，已成为国内财务数学研究热门课题。以股票市场而言，人们总希望比别人早一步掌握行情的脉动，以获取最高的报酬率，然而，影响股市加权股价指数波动的因素众多，要如何进行趋势分析与预测，是很多学者相当感兴趣与研究的主题。本文考虑以模糊统计方法，作模糊时间数列的趋势分析与预测。其望应用模糊统计分析方法比传统的时间数列分析方法能得到更合理的解释，且预测结果可以提供决策者更多的信息，做出正确的决策。最后以台湾地区加权股票指数为例，做一实证上的详细探讨。     

An optimization technique based on a vector autoregression model with state space representation: application to Ukrainian cargo transport data

This paper proposes to forecast indicators of the Ukrainian cargo transport system, taking into account their relations with macroeconomic indicators. Increased forecast accuracy at a priori information uncertainty is attained through an optimization technique, starting with a Vector Autoregression (VAR) model of observed multiple time series, its state space representation and subsequent adaptive filtering. The adaptive filter, earlier proposed by the authors, minimizes forecasting errors. Under an optimization criterion, the information divergence of Kullback–Leibler between probability distributions of real values and their estimations is established. The main advantage of the proposed technique is connected with the opportunity to estimate future values of multiple time series even in presence of structural breaks (describing the changes of the status ‘before crisis’ / ‘after crisis’). The observations are available from 2003:1–2011:12, the analysis is performed for the period 2003:1–2011:9. In-sample forecasting of multiple time series of cargo volumes transferred by different transport modes and two macro indicators is compared with the forecast based on a VAR model. In-sample forecast is realized for the last three months 2011:10–2011:12.     

Analysis of a gas field development model with an infinite planning horizon

We consider a nonlinear optimal control problem with an infinite planning horizon, which extends a dynamic gas field development model by taking into account a gas price forecast. (The prices varies in time.) The solution is constructed on the basis of the Pontryagin maximum principle. To prove the optimality of the extremal solution, we use the theorem on sufficient optimality conditions in terms of constructions of the Pontryaginmaximum principle. We discuss the problem of constructing an optimal solution by dynamic programming.     

基于贝叶斯向量自回归的区域经济预测模型:以青海为例

由于缺乏足够的观测数据等原因,常规的区域经济预测模型在我国难以获得预期的预测效果,而贝叶斯向量自回归(BVAR)模型将变量的统计性质作为参数的先验分布引入到传统的VAR模型中,能够克服自由度过少的问题,以青海为例,本文建立了一个BVAR模型,并引入了全国GDP和中央政府转移支付作为外生变量以描述国民经济与区域经济的联系...     

Towards a methodology for measuring the true degree of efficiency in a speculative market

Betting markets have drawn much attention in the economics, finance and operational research literature because they provide a valuable window on the manner in which individuals use information in wider financial markets. One question that has received particular attention is to what extent individuals discount information in market prices. The predominant approach to explore this issue involves predictive modeling to forecast market outcomes and examining empirically whether abnormal returns can be made by employing these forecasts. It is argued here that present practices to assess such forecasting models, including the use of point estimates and information, which would not be available in practice (at the forecasting stage) and failing to update forecasting models with information from the recent past, may give rise to misleading conclusions regarding a market's informational efficiency. Hypotheses are developed to conceptualize these views and are tested by means of extensive empirical experimentation using real-world data from the Hong Kong horserace betting market. Our study identifies several sources of bias and confirms that current practices may not be relied upon. A more appropriate modeling procedure for assessing the true degree of market efficiency is then proposed.     

Forecasting portfolio returns using weighted fuzzy time series methods

We propose using weighted fuzzy time series (FTS) methods to forecast the future performance of returns on portfolios. We model the uncertain parameters of the fuzzy portfolio selection models using a possibilistic interval-valued mean approach, and approximate the uncertain future return on a given portfolio by means of a trapezoidal fuzzy number. Introducing some modifications into the classical models of fuzzy time series, based on weighted operators, enables us to generate trapezoidal numbers as forecasts of the future performance of the portfolio returns. This fuzzy forecast makes it possible to approximate both the expected return and the risk of the investment through the value and ambiguity of a fuzzy number.We incorporate our proposals into classical fuzzy time series methods and analyze their effectiveness compared with classical weighted fuzzy time series models, using historical returns on assets from the Spanish stock market. When our weighted FTS proposals are used to point-wise forecast portfolio returns the one-step ahead accuracy is improved, also with respect to non-fuzzy forecasting methods.     

On the optimality of threshold control in queues with model uncertainty

We consider a single-stage queuing system where arrivals and departures are modeled by point processes with stochastic intensities. An arrival incurs a cost, while a departure earns a revenue. The objective is to maximize the profit by controlling the intensities subject to capacity limits and holding costs. When the stochastic model for arrival and departure processes are completely known, then a threshold policy is known to be optimal. Many times arrival and departure processes can not be accurately modeled and controlled due to lack of sufficient calibration data or inaccurate assumptions. We prove that a threshold policy is optimal under a max–min robust model when the uncertainty in the processes is characterized by relative entropy. Our model generalizes the standard notion of relative entropy to account for different levels of model uncertainty in arrival and departure processes. We also study the impact of uncertainty levels on the optimal threshold control.     

Forecasting seasonal time series data: a Bayesian model averaging approach

A flexible Bayesian periodic autoregressive model is used for the prediction of quarterly and monthly time series data. As the unknown autoregressive lag order, the occurrence of structural breaks and their respective break dates are common sources of uncertainty these are treated as random quantities within the Bayesian framework. Since no analytical expressions for the corresponding marginal posterior predictive distributions exist a Markov Chain Monte Carlo approach based on data augmentation is proposed. Its performance is demonstrated in Monte Carlo experiments. Instead of resorting to a model selection approach by choosing a particular candidate model for prediction, a forecasting approach based on Bayesian model averaging is used in order to account for model uncertainty and to improve forecasting accuracy. For model diagnosis a Bayesian sign test is introduced to compare the predictive accuracy of different forecasting models in terms of statistical significance. In an empirical application, using monthly unemployment rates of Germany, the performance of the model averaging prediction approach is compared to those of model selected Bayesian and classical (non)periodic time series models.