An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting

For forecasting nonstationary and nonlinear energy prices time series, a novel adaptive multiscale ensemble learning paradigm incorporating ensemble empirical mode decomposition (EEMD), particle swarm optimization (PSO) and least square support vector machines (LSSVM) with kernel function prototype is developed. Firstly, the extrema symmetry expansion EEMD, which can effectively restrain the mode mixing and end effects, is used to decompose the energy price into simple modes. Secondly, by using the fine‐to‐coarse reconstruction algorithm, the high‐frequency, low‐frequency and trend components are identified. Furthermore, autoregressive integrated moving average is applicable to predicting the high‐frequency components. LSSVM is suitable for forecasting the low‐frequency and trend components. At the same time, a universal kernel function prototype is introduced for making up the drawbacks of single kernel function, which can adaptively select the optimal kernel function type and model parameters according to the specific data using the PSO algorithm. Finally, the prediction results of all the components are aggregated into the forecasting values of energy price time series. The empirical results show that, compared with the popular prediction methods, the proposed method can significantly improve the prediction accuracy of energy prices, with high accuracy both in the level and directional predictions. Copyright © 2016 John Wiley & Sons, Ltd.     

A Time‐Simultaneous Prediction Box for a Multivariate Time Series

A sample‐based method in Kolsrud (Journal of Forecasting 2007; 26 (3): 171–188) for the construction of a time‐simultaneous prediction band for a univariate time series is extended to produce a variable‐ and time‐simultaneous prediction box for a multivariate time series. A measure of distance based on the L_∞ ‐norm is applied to a learning sample of multivariate time trajectories, which can be mean‐ and/or variance‐nonstationary. Based on the ranking of distances to the centre of the sample, a subsample of the most central multivariate trajectories is selected. A prediction box is constructed by circumscribing the subsample with a hyperrectangle. The fraction of central trajectories selected into the subsample can be calibrated by bootstrap such that the expected coverage of the box equals a prescribed nominal level. The method is related to the concept of data depth, and thence modified to increase coverage. Applications to simulated and empirical data illustrate the method, which is also compared to several other methods in the literature adapted to the multivariate setting. Copyright © 2015 John Wiley & Sons, Ltd.     

Backward‐in‐Time Selection of the Order of Dynamic Regression Prediction Model

We investigate the optimal structure of dynamic regression models used in multivariate time series prediction and propose a scheme to form the lagged variable structure called Backward‐in‐Time Selection (BTS), which takes into account feedback and multicollinearity, often present in multivariate time series. We compare BTS to other known methods, also in conjunction with regularization techniques used for the estimation of model parameters, namely principal components, partial least squares and ridge regression estimation. The predictive efficiency of the different models is assessed by means of Monte Carlo simulations for different settings of feedback and multicollinearity. The results show that BTS has consistently good prediction performance, while other popular methods have varying and often inferior performance. The prediction performance of BTS was also found the best when tested on human electroencephalograms of an epileptic seizure, and for the prediction of returns of indices of world financial markets.Copyright © 2013 John Wiley & Sons, Ltd.     

Dynamic Latent Class Model Averaging for Online Prediction

We consider the problem of online prediction when it is uncertain what the best prediction model to use is. We develop a method called dynamic latent class model averaging, which combines a state‐space model for the parameters of each of the candidate models of the system with a Markov chain model for the best model. We propose a polychotomous regression model for the transition weights to assume that the probability of a change in time depends on the past through the values of the most recent time periods and spatial correlation among the regions. The evolution of the parameters in each submodel is defined by exponential forgetting. This structure allows the ‘correct’ model to vary over both time and regions. In contrast to existing methods, the proposed model naturally incorporates clustering and prediction analysis in a single unified framework. We develop an efficient Gibbs algorithm for computation, and we demonstrate the value of our framework on simulated experiments and on a real‐world problem: forecasting IBM's corporate revenue. Copyright © 2014 John Wiley & Sons, Ltd.     

支持向量机在围岩变形监测中的应用

根据支特向量机优越的非线性拟合性能,建立变形量的时间序列预测模型,滚动预测围岩变形量,提高了预测模型的训练速度和预测推广能力。该方法用于西乡-固戍盾构段围岩变形预测,并与BP神经网络预测进行比较。结果表明这种模型可预测区间较长且具有较高的准确度,能够科学地指导现场施工和监测。     

基于区域叠合有限元技术预测三维编织复合材料弹性性能

本文基于区域叠合有限元技术预测三维四向编织复合材料弹性性能。在该方法中,分别建立相互独立的纤维增强相有限元模型和复合材料整体区域(包括所有增强相和基体相所占几何空间)有限元模型,两相模型在空间叠合组成复合材料模型,运用节点自由度耦合技术使两相模型满足变形协调关系,通过对两相模型赋予适当的材料属性以使所建复合材料模型具有与实际复合材料等效的力学特性。该方法较传统方法显著缩短了建模时间,降低了建模难度。区域叠合法的数值模拟结果与传统方法预测结果一致。本文的研究为进一步研究编织复合材料非线性宏观力学特性和渐进损伤过程模拟奠定基础。     

基于PC的SIMD技术在1／4像素精度运动预测中的应用

在最新的视频编码标准H．264／AVC中，运动预测的精度达到1／4像素。为了提高运动预测的速度，要求实现运动预测过程的SIMD并行运算，其间遇到的最大问题就是1／4像素精度参考图像的传统储存方式不适合并行操作，针对此问题提出了一种新的适合并行操作的参考图像组织方法，并采用Intel MMX和SSE技术实现了运动预测过程的SIMD并行运算，减少了整个运动预测过程消耗的时间。     

多项式曲线法在公安边防情报分析与预测中的应用

介绍了多项式曲线法的基本原理、分类和特点,讨论了建模预测的过程,通过对全国口岸出入境交通运输工具数量变化规律的研究,说明了多项式曲线法在公安边防情报分析与预测中的应用.     

Performance prediction for Grid workflow activities based on features-ranked RBF network

Accurate performance prediction of Grid workflow activities can help Grid schedulers map activities to appropriate Grid sites.This paper describes an approach based on features-ranked RBF neural network to predict the performance of Grid workflow activities.Experimental results for two kinds of real world Grid workflow activities are presented to show effectiveness of our approach.