Reducing uncertainty in short-term projections: Linkage of monthly and quarterly models

This paper shows how monthly data and forecasts can be used in a systematic way to improve the predictive accuracy of a quarterly macroeconometric model. The problem is formulated as a model pooling procedure (equivalent to non-recursive Kalman filtering) where a baseline quarterly model forecast is modified through ‘add-factors’ or ‘constant adjustments’. The procedure ‘automatically’ constructs these adjustments in a covariance-minimizing fashion to reflect the revised expectation of the quarterly model's forecast errors, conditional on the monthly information set. Results obtained using Federal Reserve Board models indicate the potential for significant reduction in forecast error variance through application of these procedures.     

基于季节性自回归积分滑动平均与深度学习长短期记忆神经网络的降水量预测

降水量数据是一种非线性、非平稳的时序序列,传统的方法较难获取数据的变化规律,深度学习长短期记忆(long short-term memory, LSTM)能较好地处理多要素变量与降水量之间的非线性关系。利用扬州市区1960—2019年8种气象基本要素数据,采用传统季节性自回归积分滑动平均(seasonal autoregressive integrated moving average, SARIMA)模型和深度学习LSTM神经网络方法对降水量数据进行预测比对,并着重分析了LSTM在不同类型不同输入与输出模式形态下的预测水平差异。结果表明：传统的SARIMA模型中静态模式较动态模式能更好地反映出扬州市区月降水量数据变化趋势,且与实际值差距较小。动态模式容易造成误差累积或整体易呈现周期性稳态变化,实时性欠缺。深度学习LSTM多输入单输出动态预测模式下,完整周期的数据输入可以让神经网络更好地学习数据的变化规律。然而将多个周期数据作为一个输入单位,易造成模型过拟合。LSTM模型(预期回顾look＿back=12)对扬州市区月降水量预测精度优于传统的SARIMA模型,均方根误差(root me...     

Forecast intervals of net migration: the case of the Netherlands

This study addresses problems concerning the forecasting of net migration in the preparation of population forecasts. "As the width of forecast intervals for migration in single years differs strongly from that of an interval for average migration during the forecast period, it is important that the forecaster indicates which type of interval is presented. A comparison of forecast intervals for net migration obtained from an ARIMA model to intervals in official Dutch national population forecasts shows that the uncertainty on migration has been underestimated in past official forecasts."     

浅谈艾里克湖引水工程项目区水土流失预测

根据《开发建设项目水土保持方案技术规范》,水土流失预测范围为工程永久占地和临时占地。根据艾里克湖引水工程建设过程中扰动和破损地面的方式、位置,造成水土流失特点,防治轻重缓急等进行水土流失防治分区。通过对工程现场调查,将项目区分为8个水土流失防治分区,分别为：取水构筑物区、管线区、调节设施区、建设附属区、弃渣场区、道路区、工程管理区和高压线路区。根据各水土流失防治区的水土流失特点、危害程度,防治措施采取治理与防护相结合、工程措施与植物措施相结合、治理水土流失与美化、绿化相结合。统筹布局各类水土保持措施,以形成完整的水土保持防治体系。     

黄山市2000年工农业产值与结构的灰色预测研究

本文应用灰色系统理论的GM(1.1)预测模型,对黄山市2000年工农业产值和农业部门产值结构进行了预测研究,揭示了黄山市90年代工农业发展的方向和途径。     

The importance of the forecast

Careful forecasts, as accurate as possible, are central to the successful implementation of policy. There are fundamental reasons why policy makers cannot ‘play by ear’, adjusting policy quickly to each unexpected deviation in economic outcomes. Specific incidents are described where economic policy went awry because of faulty forecasts. The policy process is described in detail to show precisely where the forecast enters. Forecasting as a validation tool for establishing credibility in policy formation is analysed and discussed. Some estimated measure of forecast accuracy is presented, together with commentary on the necessary degrees of precision for successful implementation of policy.     

灰色模型GM(1,1)在物价指数预测中的应用

本文介绍了灰色模型ＧＭ（１,１）的理论和方法,并应用这种方法对某地区１９９６年第三季度的零售物价指数进行了预测     

松藻矿区石壕井田东翼Ⅱ区煤层涌水量预测

利用涌水量预测方法,分析了石壕煤矿技改扩能后,矿井设计生产能力由90万t／a,提高到180万t/a的生产能力。利用现有生产水平涌水量的参考资料,对开采东翼Ⅱ区煤层-200标高水平开采提高到矿井生产能力180万t／a的涌水量预测。可供类似矿井涌水量预测提供参考。     

基于组合预测模型的飞机刹车系统性能趋势预测分析

为了对飞机刹车系统进行性能趋势预测分析,提出一种灰色关联分析确定权重的组合预测方法。首先,利用BP神经网络(back propagation network,BP)对刹车片的累积磨损量进行预测,得到网络输出序列与向后预测序列。对于灰色预测(grey model,GM)模型利用粒子群(particle swarm optimization,PSO)对其优化;用粒子群优化灰色模型(particle swarm optimization-grey model,PSO-GM)进行预测得到拟合序列与向后预测序列。在此基础上对BP网络输出序列、PSO-GM(1,1)拟合序列与原始数据序列进行灰色关联分析,确定组合加权的权重。最后对各预测模型的向后预测序列用灰色关联分析法得到的权重进行组合加权,得到最终的刹车片累积磨损量趋势预测值。仿真结果表明,采用灰色关联分析确定权重的组合预测方法具有比单预测模型更好的趋势预测效果,具有对刹车系统性能趋势预测分析很好的实际应用价值。     

基于高斯间距核回归的产品设计时间预测

为克服产品设计时间预测中的小样本和异方差噪音问题,建立一种基于高斯间距核回归(Gaussian margin kernel regression,GMKR)预测模型。首先,假定核函数回归模型的权重向量服从高斯分布,利用相对熵与输出概率密度的自然对数和设计优化目标,构建GMKR模型;然后,假设高斯分布的协方差阵为对角矩阵以简化GMKR模型,并利用粒子群算法求解相应优化问题。最后,以注塑模具设计的实例进行分析,结果表明基于GMKR的时间预测模型可行有效。