Should fixed coefficients be re-estimated every period for extrapolation?

This paper demonstrates that forecast accuracy is not necessarily improved when fixed-coefficient models are sequentially re-estimated and used for prediction, after updating the database with the latest observation(s). It is argued that although sequential estimation may minimize the variances of predictors based on some classes of estimators, sequential estimation does not necessarily yield accurate predictions (i.e. predictions that are close to actual realizations). Minimizing the mean squared prediction error about the actual realization is a necessary condition for maximizing the probability that one predictor is more accurate than others. This minimization need not require, and may even exclude, the most recent data. It has been shown by an example that a prediction based on a nonsequential estimate of a stochastically varying coefficient model is superior to predictions based on several sequential estimates of the fixed-coefficient models, including a random walk model.     

Bootstrapping forecast intervals: An application to AR(p) models

Forecast intervals typically depend upon an assumption of normal forecast errors due to lack of information concerning the distribution of the forecast. This article applies the bootstrap to the problem of estimating forecast intervals for an AR(p) model. Box-Jenkins intervals are compared to intervals produced from a naive bootstrap and a bias-correction bootstrap. Substantial differences between the three methods are found. Bootstrapping an AR(p) model requires use of the backward residuals which typically are not i.i.d. and hence inappropriate for bootstrap resampling. A recently developed method of obtaining i.i.d. backward residuals is employed and was found to affect the bootstrap prediction intervals.     

虚拟井测井曲线的预测技术

根据邻域内有限资料井的测井资料，可以预测研究区域内任意空间位置的测井参数值，进而绘制出任意井位的虚拟井测井曲线或地质解释成果图，介绍了预测方法的基本原理，以及实施预测技术时的资料预处理方法和预测结果输出方式，并用一个油田１７口井的测井资料预测虚拟井的测井参数，结果表明，这种预测技术是切实可行和经济有效的。     

ARARMA models for time series analysis and forecasting

Methods of time series forecasting are proposed which can be applied automatically. However, they are not rote formulae, since they are based on a flexible philosophy which can provide several models for consideration. In addition it provides diverse diagnostics for qualitatively and quantitatively estimating how well one can forecast a series. The models considered are called ARARMA models (or ARAR models) because the model fitted to a long memory time series (t) is based on sophisticated time series analysis of AR (or ARMA) schemes (short memory models) fitted to residuals Y(t) obtained by parsimonious‘best lag’non-stationary autoregression. Both long range and short range forecasts are provided by an ARARMA model Section 1 explains the philosophy of our approach to time series model identification. Sections 2 and 3 attempt to relate our approach to some standard approaches to forecasting; exponential smoothing methods are developed from the point of view of prediction theory (section 2) and extended (section 3). ARARMA models are introduced (section 4). Methods of ARARMA model fitting are outlined (sections 5,6). Since‘the proof of the pudding is in the eating’, the methods proposed are illustrated (section 7) using the classic example of international airline passengers.     

Role of Ferrel cell in daily variability of Northern Hemisphere Annular Mode

The Northern Hemisphere Annular Mode(NAM) represents the zonally symmetric planetary-scale atmospheric mass fluctuations between middle and high latitudes, whose variations have shown a large impact on other components of the climate system. Previous studies have indicated that the NAM is correlated with the Ferrel cell in their monthly or longer timescale variability.However, there have been few studies investigating their connections at daily timescale, though daily variability of NAM has been suggested to be an important component and has significant implication for weather forecast. The results from this study demonstrate that variability of the Ferrel cell leads that of the NAM by about 1–2 days. This statistically identified temporal phase difference between NAM and Ferrel cell variability can be elucidated by meridional mass redistribution. Intensified(weakened)Ferrel cell causes anomalously smaller(larger) poleward mass transport from the middle to the high latitudes,resulting in an increase(a decrease) in mass in the middle latitudes and a decrease(an increase) in the high latitudes.As a consequence, anomalously higher(lower) poleward pressure gradient forms and the NAM subsequently shifts to a positive(negative) phase at a time lag of 1–2 days. The findings here would augment the existing knowledge for better understanding the connection between the Ferrel Cell and the NAM, and may provide skillful information for improving NAM as well as daily scale weather prediction.     

基于进化神经网络的RNA二级结构预测方法

本文提出了一种基于进化神经网络预测RNA二级结构的方法.该方法利用进化算法优化了神经网络结构和权值,改善了传统BP人工神经网络容易陷入局部最优等缺陷.实验结果表明该算法能够获得一个最优结构和权值的神经网络,利用该网络去预测不带伪结的RNA二级结构能获得理想的效果,敏感性和相关系数都有所提升,证明了该改进算法的可行性.     

灰色预测模型在高校非正常伤亡事件中的应用

高校非正常伤亡事件日渐增多,对高校正常的管理模式提出了严峻的考验。由于非正常伤亡事件的发生时间具有不确定性或随机性,故针对这一特点,依据云南某高校2002至2012年之间在其校园内发生的非正常伤亡事件的数据,通过利用灰色模型GM(1,1)来预测未来发生伤亡事件的年份,得出该校下次出现较严重非正常伤亡事件的年份是2014年,为此提出高校务必加强防范管理的一些措施。     

支持向量机模型在高校毕业生供给预测中的应用

 以广东省高校本科毕业生供给数据为研究对象,分别构建了多元线性回归、时序外推、灰色预测和支持向量回归等模型。在此基础上,通过对几种预测模型的比较分析,从中优选出预测精度较高的模型。结果表明,支持向量回归模型可以作为预测未来高校本科毕业生供给的有效工具。     

等温吸附曲线在四川威远页岩气区块的应用与表征

基于四川威远页岩储层条件,在90℃下开展了对四川威远区块页岩气等温吸附实验,分析了影响吸附的主要因素。结合生产实际阐述了吸附气在生产中的变化规律。同时应用吸附势理论,建立不同深度下的吸附模型,对不同深度下的吸附气量进行了预测。研究表明,当地层压力较高时,解吸附气体较少,主要是游离气在贡献,随着地层压力降至10 MPa后,吸附气解析速率逐渐增大直至吸附气起主导作用;黏土含量越高,黏土与有机质的竞争吸附中占主导;吸附气含量越小,而TOC越高,有机质与黏土竞争吸附中占主导;吸附气量越高,另外压力越高、温度越低,吸附气量越高;基于吸附势理论的吸附模型比Langmuir等温吸附模型具有较好的拟合程度,具有较强的适用性;对异常高压的页岩气藏而言,随着深度的增加,吸附气含量逐渐增加,并未出现明显的极值,温度对吸附气量的影响没有压力的影响大,压力对吸附气的含量起主导作用。     

落叶松木材干燥质量的多重多元回归模型研究

选取落叶松为干燥对象,选取干球温度、干球湿度、介质循环风速、干燥时间作为自变量,选取含水率、应力作为因变量,建立多重多元回归模型,预测含水率和应力的变化.并采用预测方差验证了所建回归模型具有较高的预测能力.