基于非线性跟踪—微分器的金融时间序列预测

基于非线性跟踪-微分器的基本原理,开拓性地用二阶离散和三阶离散非线性跟踪-微分器对上证综指进行了预测.从预测结果来看,二阶非线性离散跟踪-微分器预测相对误差控制在8%以内,三阶离散非线性跟踪-微分器预测相对误差控制在5%以内,显示了较好的预测效果.     

基于多输出支持向量回归算法的股市预测

提出了一种新的多输出支持向量回归算法,给出了定义在超球上的损失函数,并将训练SVM转化为迭代解线性方程组,在求解过程中采用边计算边使矩阵降阶的方法,加快了运算速度.建立了该算法应用于股市预测的模型,对上证指数的建模与预测表明:与单输出支持向量回归算法建立的模型相比,该算法具有更好的整体预测精度和抗噪性能,是对股市进行分析和预测的一种可行而有效的方法.     

高韧性易燃特厚煤层巷道自燃监测预报技术

针对磁窑堡二矿自然发火期短且为高韧性特厚易自燃煤层,且综放面回风顺槽与多个旧巷贯通或相邻,使得巷道掘进及回采期间自燃危险性增大的特点,对巷道掘进期间实施了有效的防灭火措施方案,保障综放面安全顺利开采。     

基于粒子群算法的短期电力负荷预测

根据电力负荷的主要影响因素,考虑了休息日和气候因素的影响,建立了基于粒子群算法(PSO)的级联网络短期负荷预测模型.通过粒子群算法对级联网络的训练进行优化,提高模型的运算速度.结果表明,该方法预测精度较高,效果较好.     

广东电网公司2006年电力市场模拟交易分析

研究广东电网公司电力市场模拟交易问题.分析了2006年广东电网的模拟交易数据,指出了在电力市场交易中可能存在的问题并提出了相应的对策和建议.     

高密度电阻率成像探测法在地质超前预报中的应用

通过大理至丽江线松桂2号隧道地质超前预报的实例，介绍了高密度电阻率成像探测法在地质超前预报中的成功运用。     

Traditional versus novel forecasting techniques: how much do we gain?

This article applies two novel techniques to forecast the value of US manufacturing shipments over the period 1956–2000: wavelets and support vector machines (SVM). Wavelets have become increasingly popular in the fields of economics and finance in recent years, whereas SVM has emerged as a more user‐friendly alternative to artificial neural networks. These two methodologies are compared with two well‐known time series techniques: multiplicative seasonal autoregressive integrated moving average (ARIMA) and unobserved components (UC). Based on forecasting accuracy and encompassing tests, and forecasting combination, we conclude that UC and ARIMA generally outperform wavelets and SVM. However, in some cases the latter provide valuable forecasting information that it is not contained in the former. Copyright © 2008 John Wiley & Sons, Ltd.     

基于灰色预测模型的交通环境承载力研究

文章通过对交通环境中的环境承载力、环境容量进行研究,从系统工程的角度出发,基于灰色预测模型,提出一种确定交通承载力的方法并进行了应用。结果表明,该方法对交通环境规划,尤其是环境承载力的确定,提供了科学依据。     

Forecasting value‐at‐risk with a parsimonious portfolio spillover GARCH (PS‐GARCH) model

Accurate modelling of volatility (or risk) is important in finance, particularly as it relates to the modelling and forecasting of value‐at‐risk (VaR) thresholds. As financial applications typically deal with a portfolio of assets and risk, there are several multivariate GARCH models which specify the risk of one asset as depending on its own past as well as the past behaviour of other assets. Multivariate effects, whereby the risk of a given asset depends on the previous risk of any other asset, are termed spillover effects. In this paper we analyse the importance of considering spillover effects when forecasting financial volatility. The forecasting performance of the VARMA‐GARCH model of Ling and McAleer (2003), which includes spillover effects from all assets, the CCC model of Bollerslev (1990), which includes no spillovers, and a new Portfolio Spillover GARCH (PS‐GARCH) model, which accommodates aggregate spillovers parsimoniously and hence avoids the so‐called curse of dimensionality, are compared using a VaR example for a portfolio containing four international stock market indices. The empirical results suggest that spillover effects are statistically significant. However, the VaR threshold forecasts are generally found to be insensitive to the inclusion of spillover effects in any of the multivariate models considered. Copyright © 2008 John Wiley & Sons, Ltd.     

Allocating the weights in the linear opinion pool

A standard approach to the combination of probabilistic opinions involves taking a weighted linear average of the individuals distributions. This paper reviews some of the possible interpretations that have been proposed for these weights in the literature on expert use. Several paradigms for selecting weights are also considered. Special attention is devoted to the Bayesian mechanism used for updating expert weights in the face of new information. An asymptotic result is proved which highlights the importance of choosing the initial weights carefully.