基于ARMA模型的双路径差异性分析

拟通过单向时延的历史记录对其未来趋势进行预测。首先,通过粗粒度和细粒度两种方式采集世界各地若干目标节点的单向时延作为原始数据;然后,结合多种预测模型的特点和原始数据的内在属性,提出一种基于ARMA模型的双路径差异性分析方法;最后选取合理的预测窗口对方法的准确性进行验证。结果表明,该预测方法是正确、合理的,与灰色预测模型相比能更有效地预测单项时延差序列。     

Effect of spatial variability on arma modelling of ground motion

The Autoregressive-Moving Average (ARMA) process is an efficient method for describing ground motion through a small number of parameters. By relating these parameters to physical variables, it is possible to reasonably predict the ground motion of a site where no strong motion data has been recorded. In this paper, the effects of spatial variability on modelling strong ground motion with ARMA processes are investigated. The cross-correlation between variance stabilized series is analyzed for sites located in Mexico City. Also, the correlation and distribution of modelling parameters in the Smart-1 dense array is investigated.     

Generalized autoregressive moving average models with GARCH errors

One of the important and widely used classes of models for non-Gaussian time series is the generalized autoregressive model average models (GARMA), which specifies an ARMA structure for the conditional mean process of the underlying time series. However, in many applications one often encounters conditional heteroskedasticity. In this article, we propose a new class of models, referred to as GARMA-GARCH models, that jointly specify both the conditional mean and conditional variance processes of a general non-Gaussian time series. Under the general modeling framework, we propose three specific models, as examples, for proportional time series, non-negative time series, and skewed and heavy-tailed financial time series. Maximum likelihood estimator (MLE) and quasi Gaussian MLE are used to estimate the parameters. Simulation studies and three applications are used to demonstrate the properties of the models and the estimation procedures.     

ON WEIGHTED PORTMANTEAU TESTS FOR TIME‐SERIES GOODNESS‐OF‐FIT

Recent work in the literature has shown weighted variants of the classic portmanteau test for time series can be more powerful in many situations. In this article, we study the asymptotic distribution of weighted sums of the squared residual autocorrelations where both the sample size n and maximum lag of the statistic m grow large. Several weighting schemes are introduced, including a data‐adaptive statistic in which the weights are determined by a function of the sample partial autocorrelations. These statistics can provide more power than other portmanteau tests found in the literature and are much less sensitive to the choice of the maximum correlation lag. The efficacy of the proposed methods is further demonstrated through an analysis of Australian red wine sales.     

一种基于异常值检测和群体智能优化神经网络的电价混合预测模型

为了提高短期电价预测精度,本文提出了一种将异常值检测、时间序列分析、神经网络以及群体智能算法相结合的混合算法。作为混合算法的具体实现,文中的异常值检测利用了残差比方法和正态分布方法,群体智能优化算法选取了粒子群(PSO)算法和布谷鸟(CS)算法。作为实例研究,本文将混合模型应用用于澳大利亚新南威尔士州短期电价预测中,结果表明,混合预测方法能在一定程度上提高模型的预测精度。     

Statistical modeling of mains zero crossing variation in powerline communication

In this paper, authors investigate statistical modeling of measured mains Zero Crossing (ZC) non idealities in narrowband powerline communication systems (PLC) using time series approach based on autoregressive moving average model (ARMA). For this purpose, Box Jenkins analysis steps are deployed for detected ZC impulsive variations. Estimated models exhibit a good fit based on R-square metric reaching 90%. This modeling approach is extended to three measurement environment: in home, in lab and rural locations. Obtained models are useful for NB-PLC channel emulator implementation.     

基于ARMA模型的浮法玻璃价格预测分析

通过ARMA模型对浮法玻璃的价格作出预测,取得了较好的预测效果,从而对其价格走势形成整体把握,为节能政策制定提供数据支持,同时为建材制造商投资方向调整提供了依据。     

A mathematical model for the validation of the ground reaction force sensor in human gait analysis

A sensor validation scheme investigates a system that requires control based on monitored sensor readings in order to allow immediate corrective actions in the case of aberrations from a desired value. In this paper, the proposed sensor validation strategy is based on a mathematical model for the calculation and classification of some white noise or faults occurring in the biomechanical system during walking on an instrumented treadmill. This strategy attempts to build a predictive model from measurements to determine whether the actual values are within an expected range. Particular attention is focused on the system used to measure vertical ground reaction force (vGRF) during normal walking on the instrumented treadmill. The aim of the study is to perform sensor validation to improve the resolution and accuracy of the acquired sensor data in order to provide reliable, repeatable, reproducible information for decision making.