A mega-trend-diffusion grey forecasting model for short-term manufacturing demand

Accurate short-term demand forecasting is critical for developing effective production plans; however, a short forecasting period indicates that the product demands are unstable, rendering tracking of product development trends difficult. Determining the actual developing data patterns by using forecasting models generated using historical observations is difficult, and the forecasting performance of such models is unfavourable, whereas using the latest limited data for forecasting can improve management efficiency and maintain the competitive advantages of an enterprise. To solve forecasting problems related to a small data set, this study applied an adaptive grey model for forecasting short-term manufacturing demand. Experiments involving the monthly demand data for thin film transistor liquid crystal display panels and wafer-level chip-scale packaging process data showed that the proposed grey model produced favourable forecasting results, indicating its appropriateness as a short-term forecasting tool for small data sets.     

Forecasting and operational research: a review

From its foundation, operational research (OR) has made many substantial contributions to practical forecasting in organizations. Equally, researchers in other disciplines have influenced forecasting practice. Since the last survey articles in JORS, forecasting has developed as a discipline with its own journals. While the effect of this increased specialization has been a narrowing of the scope of OR's interest in forecasting, research from an OR perspective remains vigorous. OR has been more receptive than other disciplines to the specialist research published in the forecasting journals, capitalizing on some of their key findings. In this paper, we identify the particular topics of OR interest over the past 25 years. After a brief summary of the current research in forecasting methods, we examine those topic areas that have grabbed the attention of OR researchers: computationally intensive methods and applications in operations and marketing. Applications in operations have proved particularly important, including the management of inventories and the effects of sharing forecast information across the supply chain. The second area of application is marketing, including customer relationship management using data mining and computer-intensive methods. The paper concludes by arguing that the unique contribution that OR can continue to make to forecasting is through developing models that link the effectiveness of new forecasting methods to the organizational context in which the models will be applied. The benefits of examining the system rather than its separate components are likely to be substantial.     

BP神经网络非线性组合预测模型在海洋冰情预测中的应用

针对海洋冰情灾害的非线性复杂问题，目前已提出了多种模型对其进行预测。在此基础上，根据神经网络的非线性和良好的函数逼近特性，提出用基于BP神经网络的非线性组合预测（NN-NLCF）模型来预测海洋冰情灾害。结果表明，NN—NLCF模型与海洋冰情的非线性特性相契合，它综合利用了参与组合的多种预测模型的有效信息，因而能更客观地反映海洋冰情的发展趋势，预测结果更为稳健、精度更高，在其它自然灾害时序预测中具有一定的推广应用价值。     

我国货币乘数增长的数理分析和货币乘数预测

对货币乘数运动规律的认识以及货币乘数的预测直接关系到国家货币政策的制定.从货币乘数决定因素入手,结合实证观察和数理分析,从数学上严格证明了货币乘数趋势性增长的原因是总准备金率和通货比率的长期下降趋势,说明货币乘数的增长是金融工具创新和金融制度创新共同作用的结果.发现货币乘数的短期波动直接受基础货币的影响,依此建立的货币乘数预测方程,不仅提高了对货币乘数长期趋势性预测的准确性,而且较为准确地预测了货币乘数的季节波动性.因此,中央银行在制定金融规划的时候,应把基础货币变动对货币乘数的影响考虑在内,以更好地实现操作目标向中介目标的传导.     

组合预测的权估计及其显著性检验

提出了组合预测权系数的另一种算法,即从统计角度给出了权重的LS估计及其假设检验,旨在提高预测的精度。     

基于指数支撑度的最优组合预测模型及其性质研究

在支撑度定义的基础上,提出平均指数支撑度、平均离散度等概念,构建了平均指数支撑度最优组合预测模型,并考虑其等价的平均离散度最优组合预测模型.针对该模型,提出优性组合预测等概念,给出了非劣性组合预测和优性组合预测存在的充分条件、冗余预测方法的存在性和冗余信息的判定等结果,最后的实例说明了该模型的有效性.     

Electricity consumption prediction using a neural-network-based grey forecasting approach

Electricity consumption is an important economic index and plays a significant role in drawing up an energy development policy for each country. Multivariate techniques and time-series analysis have been proposed to deal with electricity consumption forecasting, but a large amount of historical data is required to obtain accurate predictions. The grey forecasting model attracted researchers by its ability to characterize an uncertain system effectively with a limited number of samples. GM(1,1) is the most frequently used grey forecasting model, but its developing coefficient and control variable were dependent on the background value that is not easy to be determined, whereas a neural-network-based GM(1,1) model called NNGM(1,1) has been presented to resolve this troublesome problem. This study has applied NNGM(1,1) to electricity consumption and has examined its forecasting ability on electricity consumption using sample data from the Turkish Ministry of Energy and Natural Resources and the Asia–Pacific Economic Cooperation energy database. Experimental results demonstrate that NNGM(1,1) performs well.     

An extended grey forecasting model for omnidirectional forecasting considering data gap difference

To achieve effective and efficient decision making in a highly competitive business environment, an enterprise must have an appropriate forecasting technique that can meet the requirements of both timeliness and accuracy. Accordingly, in the early stages, building a forecasting model with incomplete information and limited samples is very important to a business. Grey system theory is one of the prediction methods that can be built with a small sample and yet has a strong ability to make short-term predictions. The purpose of this study is to come up with an improved forecasting model based on the concept of this theory to enlarge the applicability of the grey forecasting model in various situations. By extending the data transforming approach, this method generalizes a building procedure for the grey model to grasp the data outline and information trend. Specifically, a novel inverse accumulating generation operator is developed to enable omnidirectional forecasting. The research utilizes observations of the titanium alloy fatigue limit along with temperature changes as raw data to verify the performance of the proposed method. The experimental results show that not only can this method expand the application scope of the grey forecasting model, but also improve its forecasting accuracy.     

组合预测模型在能源消费预测中的应用

能源的需求预测是一个复杂的非线形系统,其发展变化具有增长性和波动性,组合预测对于信息不完备的复杂经济系统具有一定的实用性.本文利用我国能源消费的历史数据,采用灰色预测的GM(1,1)模型、BP神经网络模型和三次指数平滑模型进行优化组合,建立了能源消费组合预测模型,实证分析结果表明预测值和实际结果有很好的一致性,可以作为能源消费预测的有效工具.     

Hybrid evolutionary algorithms in a SVR traffic flow forecasting model

Accurate urban traffic flow forecasting is critical to intelligent transportation system developments and implementations, thus, it has been one of the most important issues in the research on road traffic congestion. Due to complex nonlinear data pattern of the urban traffic flow, there are many kinds of traffic flow forecasting techniques in literature, thus, it is difficult to make a general conclusion which forecasting technique is superior to others. Recently, the support vector regression model (SVR) has been widely used to solve nonlinear regression and time series problems. This investigation presents a SVR traffic flow forecasting model which employs the hybrid genetic algorithm-simulated annealing algorithm (GA-SA) to determine its suitable parameter combination. Additionally, a numerical example of traffic flow data from northern Taiwan is used to elucidate the forecasting performance of the proposed SVRGA-SA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average (SARIMA), back-propagation neural network (BPNN), Holt-Winters (HW) and seasonal Holt-Winters (SHW) models. Therefore, the SVRGA-SA model is a promising alternative for forecasting traffic flow.     

目标规划在大学生学习成绩预测中的应用

大学生学习成绩预测是非常有必要的。文章首先介绍了用最小二乘法进行线性回归预测的方法，并分析了其缺点，即当存在着极端的异常情况时，往往存在着较大的预测误差。其次文章介绍了另一种建立预测方程的方法——目标规划法，并详细分析了模型的建立过程。文章最后举了一实例，将两种方法进行了比较，结果证明，当存在着异常情况时，目标规划是一种较好的预测方法。     

城市房地产预警技术研究

本文通过构建我国城市房地产预警预报系统,详细分析其中的技术要点,并运用有关城市的经验数据对系统的预警预报功能进行检验。结果表明,构建的房地产预警预报系统能准确地反映城市的房地产市场运行状况,起到预警预报的作用。     

The Organization and Improvement of Market Forecasting

Quantitative forecasting techniques are not much used in organizations. Instead, organizations rely on the judgement of managers working close to the product market. Increasingly however, developments at the interface between marketing and operations require more accurate forecasting. Quantitative marketing models have that potential. Drawing on theories from the ‘diffusion of innovation’ literature and results on ‘the barriers to effective implementation’, this paper first considers those factors that should be included in any complete evaluation of market forecasting. Using this framework and based on detailed survey work in a multi-divisional organization, the paper then describes how this company produces its market forecasts, and the perceptions of its managers as to inadequacies in the procedures. Reasons are proposed as to why quantitative forecasting techniques are not effectively used. The paper concludes with a discussion of the causes behind the organization's mismanagement of their forecasting activity and how these activities might best be improved.     

Discrete grey forecasting model and its optimization

Although the grey forecasting model has been successfully adopted in various fields and demonstrated promising results, the literatures show its performance could be further improved. For this purpose, this paper proposes a novel discrete grey forecasting model termed DGM model and a series of optimized models of DGM. This paper modifies the algorithm of GM(1, 1) model to enhance the tendency catching ability. The relationship between the two models and the forecasting precision of DGM model based on the pure index sequence is discussed. And further studies on three basic forms and three optimized forms of DGM model are also discussed. As shown in the results, the proposed model and its optimized models can increase the prediction accuracy. When the system is stable approximately, DGM model and the optimized models can effectively predict the developing system. This work contributes significantly to improve grey forecasting theory and proposes more novel grey forecasting models.     

Application of seasonal SVR with chaotic gravitational search algorithm in electricity forecasting

Hybridization chaotic mapping functions with optimization algorithms into a support vector regression model has been shown its efficient potential to avoid converging prematurely. It is deserved to explore more possibility by hybridizing with other optimization algorithms. Electricity demand sometimes demonstrates a seasonal tendency due to complicate economic activities or climate cyclic nature. This investigation presents a SVR-based electricity forecasting model which applied a novel hybrid algorithm, namely chaotic gravitational search algorithm (CGSA), to improve the forecasting performance. The proposed CGSA employs the chaotic local search by logistic chaotic mapping function in the iteration of the original GSA to search and refine the current best solution. In addition, seasonal mechanism is also applied to deal with seasonal electricity tendency. A numerical example from an existed reference is used to illustrate the forecasting performance of the proposed SSVRCGSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than ARIMA and TF-ε-SVR-SA models.     

Fuzzy interaction regression for short term load forecasting

Electric load forecasting is a fundamental business process and well-established analytical problem in the utility industry. Due to various characteristics of electricity demand series and the business needs, electric load forecasting is a classical textbook example and popular application field in the forecasting community. During the past 30 plus years, many statistical and artificial intelligence techniques have been applied to short term load forecasting (STLF) with varying degrees of success. Although fuzzy regression has been tried for STLF for about a decade, most research work is still focused at the theoretical level, leaving little value for practical applications. A primary reason is that inadequate attention has been paid to the improvement of the underlying linear model. This application-oriented paper proposes a fuzzy interaction regression approach to STLF. Through comparisons to three models (two fuzzy regression models and one multiple linear regression model) without interaction effects, the proposed approach shows superior performance over its counterparts. This paper also offers critical comments to a notable but questionable paper in this field. Finally, tips for practicing forecasting using fuzzy regression are discussed.     

Integrating quantitative and qualitative forecasting approaches: organizational learning in an action research case

This article examines the integration of quantitative and judgmental forecasting, focusing on the implementation process and its impacts on the organization. To this end, the study is based on an action research case study in the cement industry. Empirical evidence highlights the critical change management issues that need to be dealt with to implement an integrated forecasting system. The implementation phase needs to be carried out carefully to gain acceptance within the organization and to provide the best results. In addition, the forecasting process and organization need to be aligned to allow a two-way flow of information from the periphery to the centre and vice versa to allow the integration of the two approaches. In this way, not only can forecasting accuracy be improved, but better knowledge and consensus within the organization can also be achieved.     

基于统一灰色生成算子的三参数离散灰色预测模型及其应用

现有三参数离散灰色预测模型的累加阶数取值范围局限于正实数,导致模型建模能力和作用空间受限。为此,论文首先引入实数域统一灰色生成算子;然后,基于统一灰色生成算子构造了新型三参数离散灰色预测模型,实现了其阶数从正实数到全体实数的拓展与优化,从而使得新型模型具备挖掘时序数据积分特性与差异信息的双重功能;最后,将该新模型应用于某装甲装备维修经费的建模,结果显示其精度优于其它同类灰色模型。本研究成果对完善灰色算子基础理论及提高灰色预测模型建模能力具有重要价值。     

基于非参数GARCH模型的中国股市波动性预测

本文采用上证综合指数和深证成份指数1997年1月2日—2005年6月30日的每日收盘价对数百分收益率为样本,运用非参数GARCH(1,1)模型研究了中国股票市场的波动性,并与参数GARCH(1,1)模型的估计结果进行了比较,最后利用六种预测误差度量指标比较了这两种模型的样本内及样本外预测能力,结果发现,非参数GARCH(1,1)模型对股市波动性的预测精度有明显提高。     

多元模糊回归预测模型及其应用

论述多元模糊回归预测模糊的建模方法,探讨该预测模型在第二代玉米螟百株卵量各动态上的应用,研究结果表明,该预测模型为害害虫群动态的中长期预测预报提供了一种新的研究方法,是一种优良的模型。