异常突发事件下交通流特征分析与预测

针对在异常突发事件下交通流会呈现的不同特性进行研究,以期实现在突发事件下的城市道路短期交通流精准预测。引入基于跳转的ARIMA模型来对突发事件下的交通流变化规律进行描述,在路网正常情况下和路网堵塞情况下,仿真算例表明,预测模型可以较好地进行道路区域网络交流注预测。     

信用转移矩阵预测模型的比较分析

对当前国际上几种主要的信用转移矩阵估计方法中所采用的预测模型进行了系统的分析,指出这些方法在模型构建中存在的一些共性问题,并通过进一步讨论,发现传统的结构建模方法是导致这些问题的关键·为此,提出了将动态建模理论引入转移矩阵预测模型的建模框架,并从理论上说明了这种建模途径能使所构建的转移矩阵预测模型具有更为可靠的理论依据,从而改善预测模型的有效性·     

属性约简在空间电力负荷预测中的应用

空间电力负荷预测是一个将总量负荷预测分配到供电小区的过程,涉及空间信息量多,影响其未来小区使用的因素也多,这就需要大量的存储空间和较长的运算时间.粗糙集是一种新型的数据分析方法,属性约简是其主要算法,它不需提供问题所需处理的数据集合之外的任何辅助信息.因此采用粗糙集(Rough Set)区分矩阵法对空间电力负荷预测中可能影响小区用地决策的相关属性进行约简,去除冗余属性,得出决定小区用地类型的决策规则,取得了较好的效果,提高了整个负荷预测效率.     

一种模糊组合预测方法

针对预测问题的不确定性,提出一种将组合预测结果扩展到置信集合的模糊组合预测方法.该方法利用概率中置信区间原理并结合模糊数概念,对观测值和各单项预测值进行模糊描述;以组合预测值对观测值的拟合度最高为目标,建立模糊规划模型,求解权重系数.该组合预测方法与单项预测方法相比,预测精度有所提高,具有一般组合预测方法的优点,并以模糊数的形式将预测结果确定在一置信集合内.最后以实例证明了该方法的有效性.     

2021年经济景气形势监测、分析和预测

我国经济增长在遭遇前所未有的疫情冲击后进入为期一年的超常快速扩张期，并于2021年2月形成扩张高峰，此后转入经济周期收缩阶段。2020年11月至2021年7月经济运行已恢复至“正常”景气区间，但需求端的恢复弱于供给端。2021年3季度的综合警情指数明显下滑，发出“偏冷”预警信号，且4季度可能继续下行,但物价总体保持稳定。预计全年GDP增长8.1%左右（两年平均增长5.2%左右），全年CPI上涨0.9%左右。建议宏观调控应做好跨周期设计，注意处理好稳增长、防风险和节能环保的关系，提高疫情应对的精准性，适度加大稳增长力度，努力保持经济在合理区间的平稳运行。     

Financial volatility modeling: The feedback asymmetric conditional autoregressive range model

An implied assumption in the asymmetric conditional autoregressive range (ACARR) model is that upward range is independent of downward range. This paper scrutinizes this assumption on a broad variety of stock indices. Instead of independence, we find significant cross‐interdependence between the upward range and the downward range. Regression test shows that the cross‐interdependence cannot be explained by leverage effect. To include the cross‐interdependence, a feedback asymmetric conditional autoregressive range (FACARR) model is proposed. Empirical studies are performed on a variety of stock indices, and the results show that the FACARR model outperforms the ACARR model with high significance for both in‐sample and out‐of‐sample forecasting.     

基于Box-Cox变换分位数回归与负荷关联因素辨识的中长期概率密度预测

中长期电力负荷预测是电力部门制定电力系统发展规划和稳定运行的重要前提.针对影响中长期电力负荷预测精度的多个因素,本文利用逐步回归方法,从众多影响负荷预测精度的关联因子中,对关键的影响因子进行辨识,并提出基于Box-Cox变换分位数回归和核密度估计相结合的概率密度预测方法,得出不同分位点下未来连续几年的概率密度预测结果,实现了对未来年用电量准确波动区间的预测.以安徽省的历史用电量和社会经济数据为例,进行仿真实验.结果表明:该方法不仅实现了中长期电力负荷概率密度预测,而且利用强关联因素提高了中长期电力负荷概率密度预测的精度,有效解决了考虑多因子的中长期电力负荷概率密度预测问题.     

Predictive ability and economic gains from volatility forecast combinations

The availability of numerous modeling approaches for volatility forecasting leads to model uncertainty for both researchers and practitioners. A large number of studies provide evidence in favor of combination methods for forecasting a variety of financial variables, but most of them are implemented on returns forecasting and evaluate their performance based solely on statistical evaluation criteria. In this paper, we combine various volatility forecasts based on different combination schemes and evaluate their performance in forecasting the volatility of the S&P 500 index. We use an exhaustive variety of combination methods to forecast volatility, ranging from simple techniques to time-varying techniques based on the past performance of the single models and regression techniques. We then evaluate the forecasting performance of single and combination volatility forecasts based on both statistical and economic loss functions. The empirical analysis in this paper yields an important conclusion. Although combination forecasts based on more complex methods perform better than the simple combinations and single models, there is no dominant combination technique that outperforms the rest in both statistical and economic terms.     

Forecasting of electricity price through a functional prediction of sale and purchase curves

This work proposes a new approach for the prediction of the electricity price based on forecasting aggregated purchase and sale curves. The basic idea is to model the hourly purchase and the sale curves, to predict them and to find the intersection of the predicted curves in order to obtain the predicted equilibrium market price and volume. Modeling and forecasting of purchase and sale curves is performed by means of functional data analysis methods. More specifically, parametric (FAR) and nonparametric (NPFAR) functional autoregressive models are considered and compared to some benchmarks. An appealing feature of the functional approach is that, unlike other methods, it provides insights into the sale and purchase mechanism connected with the price and demand formation process and can therefore be used for the optimization of bidding strategies. An application to the Italian electricity market (IPEX) is also provided, showing that NPFAR models lead to a statistically significant improvement in the forecasting accuracy.     

及时性自适应高维经济基本面建模与汇率预测分析

在人民币国际化不断推进,人民币汇率双向波动加强的背景下,构建具有优良预测能力的汇率预测模型愈发重要.参数模型对汇率预测的能力不仅取决于模型设定是否正确,还取决于模型能够同时:一方面能否迅速探测模型参数的结构性变化以使用最佳信息估计模型参数,另一方面能否及时识别模型解释变量以使用最佳解释变量对汇率进行预测.本文构建了自适应变元算法.该算法不仅能实时检测模型参数的结构性变化,探测参数的最大化同质区间,同时还能对变量进行及时识别以选择最佳模型解释变量,提高模型的预测能力.在样本外向前3至24个月的汇率预测中,自适应变元算法能显著超越随机游走,马尔可夫机制转换模型,误差修正模型,实时最优窗算法,多元自适应可变窗算法与其他经济基本面模型包括:弹性货币模型,购买力平价模型,利率平价模型,泰勒规则模型,偏移泰勒规则模型.变量选择结果显示,自"811"汇改以后,经济基本面因素决定了人民币汇率走势.中国与其他发达经济体包括欧元区,英国与日本的经济基本面同样能够决定美元兑人民币汇率走向.另外,自"811"汇改之后,人民币汇率预期相比于"811"汇改之前更易受到外部冲击的影响,合理的人民币汇率预期监管依然需要依赖于实行有管理的浮动汇率制度,防止汇率风险.