玛纳斯河年径流的灰色拓扑预测

运用灰色系统理论中的拓扑预测法,提出一种较适合于水文实测资料缺乏情况下的中长期预报方法,并利用玛纳斯河水文站的实测资料序列对该方法进行成功地验证。     

A linear benchmark for forecasting GDP growth and inflation?

Predicting the future evolution of GDP growth and inflation is a central concern in economics. Forecasts are typically produced either from economic theory‐based models or from simple linear time series models. While a time series model can provide a reasonable benchmark to evaluate the value added of economic theory relative to the pure explanatory power of the past behavior of the variable, recent developments in time series analysis suggest that more sophisticated time series models could provide more serious benchmarks for economic models. In this paper we evaluate whether these complicated time series models can outperform standard linear models for forecasting GDP growth and inflation. We consider a large variety of models and evaluation criteria, using a bootstrap algorithm to evaluate the statistical significance of our results. Our main conclusion is that in general linear time series models can hardly be beaten if they are carefully specified. However, we also identify some important cases where the adoption of a more complicated benchmark can alter the conclusions of economic analyses about the driving forces of GDP growth and inflation. Copyright © 2008 John Wiley & Sons, Ltd.     

基于相对误差的线性组合预测研究

在讨论传统的组合预测方法的基础上,对相对误差准则下的线性组合预测进行了研究和推广。分别以"相对误差平方之和最小"、"相对误差之和最小"和"最大相对误差最小"为准则,给出了9个线性组合预测模型,其中有6个线性组合预测模型是新提出的,并且讨论了模型的解法。以美国加州电力日均价为例,给出了9种线性组合预测模型的预测结果,验证了新模型的精确性和优越性。     

基于CNN-LSTM-lightGBM组合的超短期风电功率预测方法

近年来,风电装机规模逐年增加,风电数据采集量呈现规模化增长,面对海量多维、强波动的风电数据,风电功率预测精度仍面临一定的挑战。为提高风电功率预测精度,提出了基于卷积神经网络(convolutional neural networks, CNN)-长短期记忆网络(long short-term memory, LSTM)和梯度提升学习(light gradient boosting machine, lightGBM)组合的超短期风电功率预测方法。首先,分别建立CNN-LSTM和lightGBM的风电功率超短期预测模型。其中,CNN-LSTM模型采用CNN对风电数据集进行特征处理,并将其作为LSTM模型的数据输入,从而建立CNN-LSTM融合的预测模型;然后,采用误差倒数法对CNN-LSTM和lightGBM的预测数据进行加权组合,建立CNN-LSTM-lightGBM组合的预测模型;最后,采用张北曹碾沟风电场的风电数据集,以未来4 h风电功率为预测目标,验证了组合模型的有效性。预测结果表明：相较于其他3种单一模型,组合模型具有更高的预测精度。     

通辽市未来25年人口预测与分析

根据第5次内蒙古人口普查资料,运用人口系统工程与控制论的方法,预测分析通辽市未来25年的人口发展状况.结果表明,未来25年内通辽市人口规模呈现先升后降趋势;老年人口呈快速递增态势,负担系数越来越重;少儿人数持续下降,劳动年龄人口呈现逐年老化趋势.针对通辽市的人口形势提出人口控制方案,为通辽市的人口规划、计划生育及未来的社会经济发展战略提供相关的数据依据.     

大气污染预测的参数化组合算子方法

大气污染物浓度预报是一个多因素、复杂的非线性问题,以往多使用线性回归方法．根据Zimmermann提出的参数化二元组合算子方法,将其推广为多元的情形．在成因分析的基础上,建立了大气污染物浓度预测的参数化组合算子模型,适应非线性问题．实例分析比较表明,该方法是有效的．     

红脂大小蠹监测预报方法及防治

阐述了对红脂大小蠹虫情的一般调查、预测方法,结合太原市国营林场对红脂大小蠹的防治实践,总结出对红脂大小蠹虫害的防治经验。     

基于PSO参数辨识SVM的中长期径流预测研究

以径向基函数作为核函数,利用微粒群(PSO)算法的全局寻优特性进行支持向量机(SVM)的参数辩识.在微粒群搜索参数前,先对参数进行指数变换,使[0,1]和[1,∞]有着相同的搜索概率.微粒群算法的适应值函数是以支持向量机模型的推广能力为标准的,讨论了测试样本的最小误差和留一法对支持向量机学习方法推广能力的两种估计.最后...     

Unobserved components in ARCH models: An application to seasonal adjustment

The paper deals with unobserved components in ARIMA models with GARCH errors, in the context of an actual application, namely seasonal adjustment of the monthly Spanish money supply series. The series shows clear evidence of (moderate) non-linearity, which does not disappear with simple outlier correction. The GARCH structure explains reasonably well the non-linearity, and this explanation is robust with respect to the GARCH specification. We look at the time variation of the standard error of the adjusted series estimator and show how it can be measured. Next, we look at the implications this variation has on short-term monetary control. The non-linearity seems to have a small effect in practice. It is further seen that the conditional variance of the GARCH process may, in turn, be decomposed into components. In fact, the conditional variance of the money supply series is the sum of a weak linear trend, a strong non-linear seasonal component, and a moderate non-linear irregular component. This information has policy implications: for example, there are periods in the year when policy can be more assertive because information is more precise. Finally, looking at the non-linear components of the money supply it is seen how linear combinations of non-linear series can produce series that behave linearly.     

Neural network structure identification in inflation forecasting

Neural networks (NNs) are appropriate to use in time series analysis under conditions of unfulfilled assumptions, i.e., non‐normality and nonlinearity. The aim of this paper is to propose means of addressing identified shortcomings with the objective of identifying the NN structure for inflation forecasting. The research is based on a theoretical model that includes the characteristics of demand‐pull and cost‐push inflation; i.e., it uses the labor market, financial and external factors, and lagged inflation variables. It is conducted at the aggregate level of euro area countries from January 1999 to January 2017. Based on the estimated 90 feedforward NNs (FNNs) and 450 Jordan NNs (JNNs), which differ in variable parameters (number of iterations, learning rate, initial weight value intervals, number of hidden neurons, and weight value of the context unit), the mean square error (MSE), and the Akaike Information Criterion (AIC) are calculated for two periods: in‐the‐sample and out‐of‐sample. Ranking NNs simultaneously on both periods according to either MSE or AIC does not lead to the selection of the ‘best’ NN because the optimal NN in‐the‐sample, based on MSE and/or AIC criteria, often has high out‐of‐sample values of both indicators. To achieve the best compromise solution, i.e., to select an optimal NN, the preference ranking organization method for enrichment of evaluations (PROMETHEE) is used. Comparing the optimal FNN and JNN, i.e., FNN(4,5,1) and JNN(4,3,1), it is concluded that under approximately equal conditions, fewer hidden layer neurons are required in JNN than in FNN, confirming that JNN is parsimonious compared to FNN. Moreover, JNN has a better forecasting performance than FNN.