A bayesian forecasting model for sequential bidding

In this paper we consider the problem facing a company in selecting the values of bids to submit on a sequence of contracts put out to tender. A simple-to-implement Bayesian forecasting model is presented, based on a steady Dirichlet process whose states are indexed by the possible bid decisions open to the company. The model gives an explicit algorithm for calculating the state probabilities, needing only data on the lowest bid made by the company's competitors. The flexibility of the basic model makes it a potentially powerful forecasting system for use by companies bidding for contracts.     

基于BP神经网络的股市建模与决策

给出一种基于BP神经网络的股票市场建模、预测及决策方法。研制成功了股票预测与系统，只需输入当天收盘价、成交量等七个容易得到的股票信息，系统就能给出下一交易日的买卖决策。通过对综艺股份及许继电气两支股票的预测与决策实验，表明所提方法有效，且易于操作，有实际应用价值。     

区域经济发展规划预测决策支持系统的应用

结合决策支持系统的研究和区域发展规划的实际, 对区域规划中遇到的预测问题, 本文应用了一种定性与定量相结合的方法--“调模法”来解决。“调模法”是在本文开发出的一个包括数据库、模型方法库、知识库和图形库四库结构的预测决策支持系统中实现的。这种四库结构的不断完善, 又可以应用到预测以外的其他领域。     

To combine or not to combine? issues of combining forecasts

This paper addresses issues such as: Does it always pay to combine individual forecasts of a variable? Should one combine an unbiased forecast with one that is heavily biased? Should one use optimal weights as suggested by Bates and Granger over twenty years ago? A simple model which accounts for the main features of individual forecasts is put forward. Bayesian analysis of the model using noninformative and informative prior probability densities is provided which extends and generalizes results obtained by Winkler (1981) and compared with non-Bayesian methods of combining forecasts relying explicitly on a statistical model for the individual forecasts. It is shown that in some instances it is sensible to use a simple average of individual forecasts instead of using Bates and Granger type weights. Finally, model uncertainty is considered and the issue of combining different models for individual forecasts is addressed.     

Beyond heuristics and biases: A contingency model of judgemental forecasting

The conflicting viewpoints about the quality of judgemental forecasts are examined and a model is proposed that attempts to resolve the conflict. The model sees forecasts as contingent upon the repertory of forecasting strategies that the forecaster brings to the forecasting task, the strategy that he or she selects as a function of the characteristics of the task, and the rigour with which he or she applies the strategy as a function of the motivating characteristics of the environment in which the task is encountered. The implications of differences in subjects' and experimenters' assumptions about which strategies are appropriate in experimental studies are examined, as are the implications of the differences between the motivating aspects of experimental and applied settings on both performance and on the generatizability of the results of experiments to applied judgemental forecasting.     

灰色GM模型及其应用

灰色ＧＭ模型及其应用刘希强（聊城师范学院灰色系统研究所，山东２５２０００）ＧｒｅｙＭｏｄｅｌｓａｎｄＩｔｓＡｐｐｌｉｃａｔｉｏｎｓＬｉｕＸｉｑｉａｎｇ（ＬｉａｏｃｈｅｎｇＴｅａｃｈｅｒｓ’Ｃｏｌｌｅｇｅ，Ｓｈａｎｄｏｎｇ２５２０００）Ａｂｓｔｒａｃｔ...     

企业危机预测与识别模型

针对当前大部分企业危机预警模型只能报警,不能预测的现状,提出一种基于指数平滑法与模式识别的预警模型。该方法首先对企业运营的单项特征指标进行预测,然后对预测结果模式识别分类,同时判断企业的危机状态。最后,用实例验证该模型的有效性。     

基于关联度的高嵌入维混沌预测方法研究

由于现有的采用欧氏距离确定相空间最邻近点的混沌预测方法对高维混沌时间序列预测的效果不太理想 ,因而首次提出以关联度代替欧氏距离来确定相空间最邻近点的思想。通过对水文径流序列预测的验证 ,在嵌入维数逐渐增大时 ,采用所提方法比现有的方法在预测精度方面有明显的提高     

集成专家系统和神经网络的煤矿水害预测系统

首次提出了一种混合使用专家系统和神经网络的煤矿水害预测系统的形式定义和基本系统结构，知识表示方法以及推理机制，并对已在微机上实现的系统作了详细论述。实验及试运行结果表明，文中提出的模型充分发挥了专家系统和神经网络各自的长处，因而是实用，合理的，以此模型为基础构造的预测系统具有知识获取方便，预测准确度高，速度快，使用简单，便于推广的特点，为防止矿坑突水灾害事故的发生提供了一种有效的手段。     

A unified view of statistical forecasting procedures

A large number of statistical forecasting procedures for univariate time series have been proposed in the literature. These range from simple methods, such as the exponentially weighted moving average, to more complex procedures such as Box–Jenkins ARIMA modelling and Harrison–Stevens Bayesian forecasting. This paper sets out to show the relationship between these various procedures by adopting a framework in which a time series model is viewed in terms of trend, seasonal and irregular components. The framework is then extended to cover models with explanatory variables. From the technical point of view the Kalman filter plays an important role in allowing an integrated treatment of these topics.