基于深度置信网络模型框架的长期径流预报

长期径流预报是保障水利水电工程科学运行管理的重要支撑.随着社会经济的发展,生产实践对长期径流预报精度和预见期长度的更高要求与当前预报的表现出现了矛盾.该研究提出了一种基于深度置信网络(DBN)模型的考虑气候系统指数的长期径流预报框架,并探索了其在预见期1~12个月时的预报表现.结果表明:(1)基于该框架进行预见期为1~12个月的径流预报时,在测试期的纳什效率系数(NSE)值均高于0.50,平均相对误差(MRE)值均低于35%,预报精度较高,且随着预见期的延长,径流预报能力并未呈现下降趋势;(2)在各预见期下,训练期和测试期的预报效果评估指标相差不大,说明采用的基于DBN模型的径流预报框架不存在过拟合的问题,具有优秀的泛化能力;(3)在各预见期下,汛期的径流预报效果在NSE和决定系数R²指标上优于非汛期,在MRE指标上劣于非汛期.     

应急物资需求预测研究现状与发展趋势

应急物资需求预测研究是灾后应急救援的重要组成部分。为系统了解应急物资需求预测的研究现状,查阅大量国内外参考文献,对目前应急物资需求预测研究中常用的案例推理法、神经网络法和灰色系统预测法进行了讨论分析,总结了各自的优缺点及适用条件;并分析总结了近几年的研究重点方向。结果表明,应急物资需求预测在多元化、准确性、智能化等方面的进一步研究对灾后救援有着更大的现实意义。基于此,本文对应急物资需求预测未来的发展方向及研究重点进行了展望,对于应急物资需求预测未来的研究及应急救援体系的建立具有一定的参考作用。     

Combining var estimation and state space model reduction for simple good predictions

A combination of VAR estimation and state space model reduction techniques are examined by Monte Carlo methods in order to find good, simple to use, procedures for determining models which have reasonable prediction properties. The presentation is largely graphical. This helps focus attention on the aspects of the model determination problem which are relatively important for forecasting. One surprising result is that, for prediction purposes, knowledge of the true structure of the model generating the data is not particularly useful unless parameter values are also known. This is because the difficulty in estimating parameters of the true model causes more prediction error than results from a more parsimonious approximate model.     

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.     

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.     

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.     

高等网络教育市场与价值链研究

分析了我国高等网络教育市场现状、市场管制、市场结构和市场趋势等,介绍了我国高等网络教育价值链的构成和各个子环节及其作用和参与者。     

城市交通分布预测模型研究——系统平衡模型及其应用

提出了一种适用面广泛的交通分布预测系统平衡模型,该模型的建模基础是交通分布的内在规律,并能充分反映特殊因素对交通分布的影响,提高其准确性,在有完整现状ＯＤ、有部分现状ＯＤ以及无现状ＯＤ的情况下均可使用