Decision trees for forecasting

Recent years have seen an increasing cross-fertilization between the fields of decision analysis and forecasting. Decision-analytic models often require forecasts as inputs, and aspects of the Bayesian decision-theoretic framework underlying decision analysis have proved useful to forecasting, particularly in contexts where subjective judgemental inputs are required. This paper describes the use of decision tree analysis for forecasting and illustrates its use for corporate divisional forecasting and planning. A specialized decision-analytic technique, acts as events, is also described and illustrated to forecast a new product's earnings. Conclusions are drawn about the applicability of decision analysis for forecasting.     

Forecasting volatility by means of threshold models

The aim of this paper is to compare the forecasting performance of competing threshold models, in order to capture the asymmetric effect in the volatility. We focus on examining the relative out‐of‐sample forecasting ability of the SETAR‐Threshold GARCH (SETAR‐TGARCH) and the SETAR‐Threshold Stochastic Volatility (SETAR‐THSV) models compared to the GARCH model and Stochastic Volatility (SV) model. However, the main problem in evaluating the predictive ability of volatility models is that the ‘true’ underlying volatility process is not observable and thus a proxy must be defined for the unobservable volatility. For the class of nonlinear state space models (SETAR‐THSV and SV), a modified version of the SIR algorithm has been used to estimate the unknown parameters. The forecasting performance of competing models has been compared for two return time series: IBEX 35 and S&P 500. We explore whether the increase in the complexity of the model implies that its forecasting ability improves. Copyright © 2007 John Wiley & Sons, Ltd.     

On the sign of the optimal combining weights under the error-variance minimizing criterion

Building on the well-known measure of the optimal combining weights under the error-variance minimizing criterion, this note has extended the sign-determination rule to the case of combining more than two competing forecasts. The algebraic rule derived provides a quick way to check the sign of each combining weight without directly comparing the correlation and variances of individual forecasting errors.     

Artificial Neural Network for Combining Forecasts

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基于时空图注意力的短期电力负荷预测方法

准确的电力负荷预测对现代电力系统的安全经济运行至关重要.电力负荷预测可以表述为一个具有一定潜在空间依赖性的多变量时序预测问题.然而,大多数现有的电力负荷预测工作未能探索这种空间依赖关系.基于此,本文提出了一种基于时空图注意网络的短期电力负荷预测方法.提出一种基于时空图注意网络模块,该模块使用图注意层实现自适应的捕捉各用户间的潜在空间依赖性,同时使用门控卷积注意力层对各用户用电量在时间维度上进行自适应拟合,以提高网络的预测精度.实际数据实验表明,本文提出的模型整体预测精度提高明显,特别是在一定程度上缓解了长程预测精度恶化的问题,验证了所提方法的有效性与可行性.     

一种改进的遗传优化算法在网络故障预测中的应用

模糊逻辑控制器(FLC)模型简明，易于利用专家知识，适应能力强．适用于多输入、具有不确定因素、非线性系统的控制。随着系统复杂度的提高，直观经验获取难度变得越来越大．既难表述，又难使用。本文针对网络系统中故障的预测和诊断问题，将遗传算法与前馈网络结合，提出了一种基于BP的遗传优化改进型算法(RGA—BP)，并与经典BP算法结果作了比较，迭代步骤很明显的减少，精度明显提高。     

煤和瓦斯突出与太阳黑子活动的关系

本文在对鱼田堡煤矿和白皎煤矿历年突出资料进行统计和处理的基础上，研究了煤和瓦斯突出与太阳黑子活动的关系。发现煤和瓦斯突出年分布具有周期性，并与太阳黑子活动周期有关，讨论了太阳黑子活动影响煤和瓦斯突出的机理，最后就如何利用太阳，黑子活动周期预防煤和瓦斯突出提出了方法和建议。     

时间序列分析预测

将RFDE(Retarted Functional Differential Equation)和NFDE(Neutral Functional DifferentialEquation)引入到时间序列单元分析预测中,建立了RFDE和NFDE进行预测的理论基础和几个新的预测方法,并解决了其它一些预测方法难以解决的预测问题。     

基于模糊C均值聚类和神经网络的短时交通流预测方法

短时交通流预测是动态交通控制和诱导的前提。提出一种模糊C均值聚类和神经网络相结合的短时交通流预测方法。用同一组实测数据对比计算了该方法与BP神经网络预测方法、模糊神经网络预测方法分别得到的预测结果。计算结果表明：所提出的方法的预测准确性明显地高于其他两种方法。     

The predictability of stock market volatility in emerging economies: Relative roles of local,regional, and global business cycles

This paper explores the role of business cycle proxies, measured by the output gap at the global, regional, and local levels, as potential predictors of stock market volatility in the emerging BRICS nations. We observe that the emerging BRICS nations display a rather heterogeneous pattern when it comes to the relative role of idiosyncratic factors as a predictor of stock market volatility. While domestic output gap is found to capture significant predictive information for India and China particularly, the business cycles associated with emerging economies and the world in general are strongly important for the BRIC countries and weakly for South Africa, especially in the postglobal financial crisis era. The findings suggest that despite the increase in the financial integration of world capital markets, emerging economies can still bear significant exposures to idiosyncratic risk factors, an issue of high importance for the profitability of global diversification strategies.