基于小波分析的石油价格长期趋势预测方法及其实证研究

本文将小波方法引入到油价长期趋势的预测中,利用小波多尺度分析的功能,提出了一种可以较为准确地根据油价时序列预测其未来长期走势的方法。这种方法的优点在于可以准确地提取油价的长期趋势,从总体上把握油价的非线性波动特征,从而能够很好地利用油价时间序列的历史数据,开展对未来一段时期内的多步预测。实证研究中,对Brent油价开展了时间跨度为1年的趋势预测,并将预测结果与ARIMA、GARCH、Holtwinters等方法得到的结果进行了比较,表明了基于小波分析的长期趋势预测法的预测能力是其他方法所不能比拟的,反映了本文所建立的石油价格长期趋势预测方法的有效性。     

一种非线性时间序列预测模型及对原油价格的预测

针对具有非线性和不稳定性的时间序列,提出一种结合小波分解、滑动平均离散差分方程和马尔可夫方法的动态预测模型.利用小波多尺度分解将原时间序列分解到不同频率通道上,然后对分解出的低频近似小波系数利用滑动平均离散差分方程预测模型进行预测,并利用马尔可夫方法对时间序列的高频细节小波系数进行预测,最后将低频和高频的预测结果进行小波重构得到时间序列的实际预测值.原油价格的时间序列是一类典型的具有非线性和不稳定性的序列,利用此模型对WTI原油(周度)价格进行实证预测分析,分别预测WTI原油价格的整体变化趋势和周度实际原油价格.研究结果表明,此模型不但可以有效地预测时间序列的整体变化趋势,能从细节上对其进行有效的刻画,而且比其他基于小波的预测模型具有更高的预测精度.可以看出国际原油价格从整体上呈现周期性上涨趋势,并且不稳定的随机波动也会一直存在.     

短期电价预测的组合混沌方法

电力市场中,电价的变化呈现的是一种非线性的、动态开放的过程,传统的方法已很难提高其预测精度。为此,本文提出一种基于小波变换、计量经济学模型和径向基函数网络的组合混沌预测方法。首先利用小波变换将原电价序列分解、重构成概貌序列和细节序列;在此基础上,针对不同的子序列建立不同的模型,并进行预测;最后将所有子序列的预测结果求和,作为最终的预测值。对西班牙电力市场短期电价的预测表明,该方法具有很高的预测精度。     

基于小波域隐马尔可夫模型的时间序列分析-平滑、插值和预测

提出一种基于小波域隐马尔可夫模型的时间序列分析方法.首先介绍了离散小波变换;并针对小波系数进行统计建模,分别讨论了单个小波系数的混合高斯模型、不同尺度小波系数之间的隐马尔可夫树结构、模型训练及似然计算等问题;其次,提出了关于时间序列插值、平滑和预测的统一数学模型,并运用极大后验概率估计和贝叶斯原理,将小波域隐马尔可夫模型作为先验知识给出了一种分析时间序列的新方法;然后,详细推导了时间序列重建问题的Euler-Lagrange方程及对数似然的导数计算,将时间序列的插值、平滑和预测归结为一个简单线性方程的求解;最后通过期望极大化(EM)算法和共扼梯度算法进行交替迭代来计算小波域隐马尔可夫模型参数和重建时间序列.实验结果表明该方法在经济领域时间序列分析中的有效性.     

股市预测中的小波神经网络方法的研究

本文首先论述了股市时间序列中的明显随机性,可能是由于非线性确定性系统中混沌行为的缘故,利用混沌的确定性可以进行短期预测.混沌时间序列预测首先要重构相空间,接着充分利用小波变换时频分析的局部化特性,提出了一种改进的小波网络结构,探讨了股市预测模型问题.经实例验证,该方法能有效地提高预测精度,避免了人工神经网络模型和指数自回归的固有缺陷.     

基于小波包和神经网络的股票价格预测模型

股票价格是大量因素影响的综合结果,波动规律异常复杂,即使是神经网络这样强大的非线性预测工具也不堪胜任对其的准确预测。本文利用小波包理论将价格波动序列最优地分解为一系列规律较易掌握的子波动,对原始价格波动的预测也就分成神经网络对各子波动的预测。实证研究结果表明,这种小波包和神经网络相结合的股票价格预测模型预测精度明显高于小波和神经网络相结合以及直接利用价格波动预测的同类神经网络模型。     

小波包与混沌集成的股票市场预测新方法

基于小波包变换和混沌理论提出了一种股票市场建模及其预测的新方法,既能刻划时间序列的规律,又能捕捉混沌状态的特征.首先,应用小波包变换对上证综指和深证成指日收益率序列进行三层分解,分别得到第三层从低频到高频八个频率成分的时序,并在此基础上作进一步分析,结果表明中国股市存在混沌特性;然后,应用混沌理论分别建立从低频到高频八个时序的预测模型,分别对八个时序进行预测;最后,基于小波包理论对混沌模型预测的结果予以重构,实现对原始收益率序列的预测.与现有方法比较,结果表明该方法具有较高的精度,有极大的应用范围.     

违约预测的小波结构模型研究

本文针对上市公司违约预测问题,按照行业类型对我国2009年的上市企业进行分层抽样,构建了小波结构模型.小波结构模型通过应用小波变换来分解上市公司日收益序列,进而对低频序列和高频序列分别构建预测模型,再依据预测模型对未来收益进行预测,最后使用小波逆变换重构预测收益序列.通过小波结构模型可以避免时间序列模型进行收益波动预测的累加计算过程.在结合我国上市公司的实际数据对这两种模型的校验中,可以发现小波结构模型比时序结构模型在违约预测上有更好的识别力和准确度.     

中国外商直接投资时间序列趋势的实例验证

本文利用时间序列分析理论,对我国外商直接投资(FDI)的发展趋势建立了动态模型,并预测了未来几年我国FDI的发展趋势。此模型利用小波分析剔除噪声对未来预测的干扰,提高了时间序列模型的预测精度。     

基于小波包和最小二乘支持向量机的短时交通流组合预测方法研究

基于小波在处理非线性、非平稳随机信号的优势以及支持向量机在解决小样本、非线性及高维模式识别问题中的优势。笔者探讨结合小波包和最小二乘支持向量机的组合预测方法在交通流短时预测中的应用。首先介绍小波包和最小二乘支持向量机的基本原理,然后提出基于小波包和最小二乘支持向量机的交通流短时组合预测方法,并以北京市快速路的实测交通流量来验证效果,结果表明其可行性和有效性。     

预测困难与预测发展

"凡事预则立,不预则废"这充分说明预测在决策中的重要性,但由于社会、经济系统的复杂性、不确定性,预测有时是困难的,基于此点,人们不断探索新的预测方法。神经网络预测正是近年来发展起来的一种新的预测方法,本文详细探讨了预测困难后,讨论了神经网络预测的优点与不足,并对预测发展提出了自己的思路。     

Forecasting organizational changes

In this article the author argues that because of the difficulties associated with human behaviour such factors are frequently omitted from forecasts. Yet no organization can afford to disregard these factors if their forecasts are to be meaningful. The author includes illustrations of organizational change which he maintains will spread rapidly. Finally he discusses his own initial experiments in forecasting organizational change.     

Bayesian Forecasting via Deterministic Model

Rational decision making requires that the total uncertainty about a variate of interest (a predictand) be quantified in terms of a probability distribution, conditional on all available information and knowledge. Suppose the state-of-knowledge is embodied in a deterministic model, which is imperfect and outputs only an estimate of the predictand. Fundamentals are presented of two Bayesian methods for producing a probabilistic forecast via any deterministic model. The Bayesian Processor of Forecast (BPF) quantifies the total uncertainty in terms of a posterior distribution, conditional on model output. The Bayesian Forecasting System (BFS) decomposes the total uncertainty into input uncertainty and model uncertainty, which are characterized independently and then integrated into a predictive distribution. The BFS is compared with Monte Carlo simulation and ensemble forecasting technique, none of which can alone produce a probabilistic forecast that quantifies the total uncertainty, but each can serve as a component of the BFS.     

Modeling and Forecasting Realized Volatility

We provide a framework for integration of high–frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency return volatilities and return distributions. Building on the theory of continuous–time arbitrage–free price processes and the theory of quadratic variation, we develop formal links between realized volatility and the conditional covariance matrix. Next, using continuously recorded observations for the Deutschemark/Dollar and Yen/Dollar spot exchange rates, we find that forecasts from a simple long–memory Gaussian vector autoregression for the logarithmic daily realized volatilities perform admirably. Moreover, the vector autoregressive volatility forecast, coupled with a parametric lognormal–normal mixture distribution produces well–calibrated density forecasts of future returns, and correspondingly accurate quantile predictions. Our results hold promise for practical modeling and forecasting of the large covariance matrices relevant in asset pricing, asset allocation, and financial risk management applications.     

Forecasting Spanish Natural Life Expectancy

Knowledge of trends in life expectancy is of major importance for policy planning. It is also a key indicator for assessing future development of life insurance products, substantiality of existing retirement schemes, and long-term care for the elderly. This article examines the feasibility of decomposing age-gender-specific accidental and natural mortality rates. We study this decomposition by using the Lee and Carter model. In particular, we fit the Poisson log-bilinear version of this model proposed by Wilmoth and Brouhns et al. to historical (1975-1998) Spanish mortality rates. In addition, by using the model introduced by Wilmoth and Valkonen we analyze mortality-gender differentials for accidental and natural rates. We present aggregated life expectancy forecasts compared with those constructed using nondecomposed mortality rates.     

基金经理预测能力检验

基金管理者自称能够战胜市场获取超额收益,国内已有的文献也证明了这一点。本文根据詹森的基金业绩评价思想,利用周和月两种频率数据,采用更长的样本期间,对我国54只封闭式基金进行研究,结论表明基金管理者的投资结果比随机选择持有策略还差,它们并不具有对个股和市场的预测能力。     

Forecasting expenditure on capital projects

S-curves are widely used for planning, forecasting and control of cost, time and resources of a project. In this paper, a comparison of two S-curve models developed at the Department of Health and Social Security (DHSS) and Bradford University is carried out both from the viewpoint of predictive accuracy and ease of use. The models are validated using expenditure data for 21 recent U.K. health building projects. Methods of least squares is used to estimate the parameters of the two models. These parameters are categorized according to the total cost of the projects. Both the models are shown to be of comparable accuracy for fitting actual expenditure data. The DHSS model has a major advantage of simplicity of form and use, although the slightly greater mathematical complexity of the Keller-Singh model is off-set by the readily interpretable nature of its form and basic parameters. It is concluded that both or either of the models could be used by clients/contractors for effective planning and control of project costs.     

电子商务环境下预测支持系统研究

电子商务的到来极大地改变着未来市场需求模式,预测工作面临着巨大的挑战,如何适应时代的需求,实现预测工作的高效化、智能化,已越来越受到商家的重视.根据近来发展的Agent技术,讨论了电子商务环境下的预测支持系统.