基于示例的组合预测方法*

本文提出了一种基于示倒的组合预测方法，强调知识方法和数学方法的结合，提出了种算法和组合预测框架，并结合实验数据讨论了预测结果．分析了不同预测方法的不足．     

基于示例的组合预测方法

本文提出了一种基于示例的组合预测方法，强调知识方法和数学方法的结合，提出了一种算法和组合预测框架，并结合实验数据讨论了预测结果，分析了不同预测方法的不足。     

基于遗传算法的发动机需求量组合预测方法

阐述了基于改进遗传算法组合预测的原理，给出了此类组合预测的建模方法及有效性评价指标。在军用航空发动机采购需求量的预测中，运用计算机仿真的方法对比了多种预测方法，验证了组合预测法的有效性和准确性。     

基于遗传算法的发动机需求量组合预测方法

阐述了基于改进遗传算法组合预测的原理，给出了此类组合预测的建模方法及有效性评价指标。在军用航空发动机采购需求量的预测中，运用计算机仿真的方法对比了多种预测方法，验证了组合预测法的有效性和准确性。     

基于人工神经网络组合预测油田产量

油田原油产量的准确预测可以对油田的生产管理进行合理的指导。该文探讨了应用神经网络组合方法预测油田产量，对开井数、含水率、动用储量以及往年产量同未来产量之间的复杂关系建立模型。采用了两层预测系统：第一层包含两个神经网络，一个多层前馈网络和一个函数链接网络；第二层是把第一层的两个网络输出进行组合。研究了五种不同的组合算法：平均法、最小平方回归法、模糊逻辑法、自适应前馈神经网络法和自适应函数链接神经网络法。根据油品类型分为稀油、热采稠油、常规稠油和总产量四组数据，对上述方法进行了测试，结果表明应用人工神经网络的组合预测方法优于其他的预测方法，而且适用范围广。     

变结构组合预测方法在短期电力负荷预测中的应用

采用变结构组合预测方法建立短期电力负荷预测模型。变结构组合预测对多种预测方法进行整合,使得在不同的预测阶段,最优的预测方法发挥的作用最大,使预测精度大大提高。     

组合预测模型在猪肉价格预测中的应用研究

本文在分析了神经网络、灰系统和时间序列预测模型的基础上,设计了将其中两种模型组合的预测方法。该方法的主要思想是利用回归预测思想将预测分为因素预测和结果预测两部分,并分别采用不同预测模型进行预测,从而达到提高预测精度的目的。利用该方法对吉林省近期的生猪价格进行预测,实验结果表明,该方法比单个预测方法有更好的预测效果,并且通过对不同组合的实验结果的分析发现,灰系统与神经网络相结合的方法具有更高的预测精度。     

季节性组合预测模型在医院门诊量中的应用研究

医院门诊量是一个具有复杂的非线性组合特征的季节性时问序列。为解决传统时间序列预测大多数都是使用单一模型，以致影响预测精度等问题，采用了最优加权组合预测方法将季节性ARIMA乘积模型和季节性神经网络模型进行组合优化。结果表示，季节性组合预测模型在拟合精度和预测准确性方面优于任何单一预测方法，为季节性时间序列预测提供了一种新的实用方法。     

基于ARIMA与BP的水利工程投资预测模型研究

为了提高时间序列短期预测的精度,提出了把ARIMA模型和BP神经网络模型进行组合预测的思路.将该组合模型应用在南水北调在建工程项目投资预测中,利用多种定阶准则对不同ARIMA模型的预测效果进行比较,指出多种定阶准则各有利弊;然后利用BP神经网络将不同ARIMA模型预测值进行进一步组合预测.实验结果表明,组合模型充分发挥了两种模型各自的优势,比单一的预测方法具有更高的精度,在时间序列短期预测中预测效果良好.     

基于模糊变权组合预测模型的航材预测

由于航材预测的发展,每一种航材的预测模型有很多种。不同模型的预测模型体现了预测对象不同方向的发展趋势,合理地处理信息可以更加全面地预测航材需求趋势。模糊变权组合预测模型不同于传统组合预测模型,它采用权重系数为三角模糊数来组合不同预测模型结果,并以单项预测方法预测精确度指数最小为准则,集成处理得到较窄的取值范围来帮助航材预测决策。最后以实例验证方式检验该模型的有效性和准确性,具有一定的实用性。     

Applications of artificial neural networks to the nonlinear combination of forecasts

Abstract: This paper proposes artificial neural networks (ANN) as a tool for nonlinear combination of forecasts. In this study, three forecasting models are used for individual forecasts, and then two linear combining methods are used to compare with the ANN combining method. The comparative experiment using real-world data shows that the prediction by the ANN method outperforms those by linear combining methods. The paper suggests that the ANN method can be used as an alternative to conventional linear combining methods to achieve greater forecasting accuracy.     

Combining Global Antithetic Forecasts

This paper provides a summary of the theory for antithetic forecasting, and an empirical exposition. An original forecast is combined with another forecast, produced from a time series which is antithetic (negatively correlated) to the original time series. The forecasts are combined via a linear projection of the antithetic series on the original series, such that the component forecasts have negatively correlated errors. Large-scale empirical tests and benchmark comparisons demonstrate the effectiveness of combining antithetic forecasts, even as these data depart from the strict theoretical lognormality requirement of antithetic forecasting. The method is illustrated in detail, using a real time series. Antithetic forecasting is the first combining method in which the gain increases with the forecast horizon.     

An optimal estimation technique for increasing the accuracy of crop forecasts by combining remotely sensed and conventional forecast results.

Abstract

An optimal estimation (OE) technique has been used to increase the accuracy of crop acreage and yield estimates by combining results from remotely sensed (RS) data and conventional models. For crop acreage estimation the OE increased the accuracy of wheat acreage estimation when the first forecasts of the Directorate of Economics and Statistics (DES) were combined with state level RS estimates over the states of Haryana and Punjab in India.

To increase the accuracy of wheat yield forecasts an autoregressive (AR) model was developed. Results of AR model were optimally combined with RS-based estimates for Hisar and Karnal districts in Haryana, India. The OE results for a total of eight forecasts had a lower mean absolute per cent deviation than the forecasts using RS and AR approaches. The power of OE was further demonstrated by combining weather-based wheat yield model results for the state of Punjab (India) with first order AR model results, suggesting an increase in accuracy of forecasts by optimally combining results from two or more algorithms.     

网格计算环境下任务执行时间的组合预测

有效的资源调度算法提高了任务的执行时间，对优化资源的使用起着非常重要的作用。在网格计算环境下，需要用统计预测的方法对任务的执行时间进行估计。该文提出了任务执行时间的组合预测方法，以任务过去执行时间的观察值为基础，用多种预测方法对任务的执行时间进行估计，用多种预测方法得出的估计值进行组合预测，给出任务执行时间的估计值。实验表明，组合预测要优于单一模型 预测。     

Performance evaluation of weights selection schemes for linear combination of multiple forecasts

Time series modeling and forecasting are essential in many domains of science and engineering. Extensive works in literature suggest that combining outputs of different forecasting methods substantially increases the overall accuracies as well as reduces the risk of model selection. The most popular method of forecasts combination is the weighted averaging of the constituent forecasts. The effectiveness of this method solely depends on appropriate selection of the combining weights. In this paper, we comprehensively evaluate a wide variety of benchmark weights selection techniques for linear combination of multiple forecasts in terms of their prediction accuracies. Nine real-world time series from different domains and five individual forecasting methods are used in our empirical work. A robust scheme is also suggested for fairly ranking the combination methods on the basis of their forecasting performances. Our study precisely demonstrates the relative strengths and weaknesses of various benchmark linear combination techniques which evidently can be of much practical importance.     

Neural network ensemble operators for time series forecasting

The combination of forecasts resulting from an ensemble of neural networks has been shown to outperform the use of a single “best” network model. This is supported by an extensive body of literature, which shows that combining generally leads to improvements in forecasting accuracy and robustness, and that using the mean operator often outperforms more complex methods of combining forecasts. This paper proposes a mode ensemble operator based on kernel density estimation, which unlike the mean operator is insensitive to outliers and deviations from normality, and unlike the median operator does not require symmetric distributions. The three operators are compared empirically and the proposed mode ensemble operator is found to produce the most accurate forecasts, followed by the median, while the mean has relatively poor performance. The findings suggest that the mode operator should be considered as an alternative to the mean and median operators in forecasting applications. Experiments indicate that mode ensembles are useful in automating neural network models across a large number of time series, overcoming issues of uncertainty associated with data sampling, the stochasticity of neural network training, and the distribution of the forecasts.     

周相似特性下交通流组合预测方法研究

根据交通流量具有周相似的特性,构造了周相似序列。用霍特指数平滑法对周相似序列进行预测,用人工神经网络对残差部分进行预测。将指数平滑法与神经网络法相结合,以便发挥每种方法的优势,获得比单个方法更好的预测结果。实例分析表明,比单独使用ARIMA或单独使用神经网络方法,使用组合方法的预测误差最小,适合于实时的交通流预测。     

医用服务机器人关键技术分析

介绍了国内外医用服务机器人的研究现状,结合某一具体的医用服务机器人,详细阐述了医用服务机器人涉及的关键技术,并对未来的研究方向做了预测。     

Improved supply chain management based on hybrid demand forecasts

Demand forecasts play a crucial role for supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Several forecasting techniques have been developed, each one with its particular advantages and disadvantages compared to other approaches. This motivates the development of hybrid systems combining different techniques and their respective strengths. In this paper, we present a hybrid intelligent system combining Autoregressive Integrated Moving Average (ARIMA) models and neural networks for demand forecasting. We show improvements in forecasting accuracy and propose a replenishment system for a Chilean supermarket, which leads simultaneously to fewer sales failures and lower inventory levels than the previous solution.     

Forecast Combination by Using Artificial Neural Networks

One of the efficient ways for obtaining accurate forecasts is usage of forecast combination method. This approach consists of combining different forecast values obtained from different forecasting models. Also artificial neural networks and fuzzy time series approaches have proved their success in the field of forecasting. In this study, a new forecast combination approach based on artificial neural networks is proposed. The forecasts obtain from different fuzzy time series models are combined by utilizing artificial neural networks. The proposed method is applied to index of Istanbul stock exchange (IMKB) time series and the results are compared to other forecast combination methods available in the literature. As a result of the implementation, it is seen that the proposed forecast combination approach produces better forecasts than those produced by other methods.