利用ARMA模型估算功率谱的一种方法

本文将Levinson-Durbin算法和最小实现算法相结合，给出一种ARMA模型估算功率谱函数的新方法，从而避免解ARMA模型的非线性方程。     

ARMA时间序列模型在销售预测中的应用

时间序列分析是经济领域应用研究最广泛的工具之一,它用恰当的模型描述历史数据随时间变化的规律,并分析预测变量值。ARMA模型是一种最常见的重要时间序列模型,它被广泛应用到经济领域预测中。本文给出ARMA模型的三种模式和实现方法,然后结合超市销售数据揭示超市销售的规律性,并运用ARMA模型对超市销售量进行预测。     

基于小波去噪及ARMA模型的故障率预测方法研究

在现有的故障诊断与预测方法的基础上,提出了一种基于小波及ARMA模型的预测方法。首先给出了小波去噪的原理,以及ARMA模型预测的思想,结合民机故障率的特点,对实际的故障率数据进行小波去噪,保留故障发展的主要趋势,对主要趋势进行ARMA建模预测,并将预测结果与实际值进行比较。结果说明,进行小波去噪后的ARMA建模预测的结果有较高的准确率。     

估计ARMA过程功率谱的一个方法

对于时间序列分析,常会遇到功率谱(简称作谱)的估计问题.在和中给出了估计ARMA过程谱的一些方法,但涉及解非线性方程组.本文给出一个估计ARMA谱的方法,它不涉及解非线性方程,只需估计出ARMA模型中的自回归参数并用序列的自相关就可得到混合模型的谱估计.我们对此方法作了详细推导并给出了数值实例.     

应用DDS方法辨识机器人振动的模态参数

本论文应用DDS方法将机器人振动系统的运动微分方程转换到一个自回归滑动平均(ARMA)模型.根据 ARMA 模型的性质,给出了ARMA模型的特征参数与机器人振动模型之间的关系,并通过Householder-OUSEHOLDER变换的最小二乘法.对机器人振动系统的模态参数进一步识别.     

估计MA参数的多维强Gevers-Wouters算法及其在构造ARMA新息模型中的应用

提出了用多维Gevers-Wouters（G－W）算法得到稳定的滑动平均（MA）过程的一个频域充分条件,并给出了在构造ARMA新息模型中的应用,给出了保证ARMA新息模型的MA多项式矩阵稳定的一个时域充分条件,仿真结果表明,多维G－V算法具有快速收敛的性质。     

ARMA序列的版参数预报*

在关于ARMA序列新息定理的基础上,本文给出了ARMA序列的一种半参数预报方法,该方法避开对ARMA模型中MA参数作估计这一计算复杂的工作,在只须知道AR参数的情况下直接得到序列的预报值,从而使计算量大为简化同时又保证有一定的精度,本文还介绍了半参数预报方法在城市自来水负荷预报中的成功应用。     

基于ARMA和卡尔曼相结合的实际流量预测模型的研究

为了在高度复杂网络环境下,针对无线传感器网络流量预测精度偏低的问题,结合ARMA模型和卡尔曼提出了一种新的预测算法(State Prediction Algorithm based on ARMA and Kalman,SPAK)。该算法首先定义了ARMA模型分布特征,并给出该模型的理论依据。同时,通过融合ARMA与卡尔曼的预测结果提高其预测实际流量的预测精度,实现SPAK具体实现的步骤。最后,结合OPNET仿真采集流量数据,并使用Matlab对算法结果进行仿真,并对比FARIMA模型MF-FIR模型性能,结果发现该算法可以提前预测拥塞情况,提前进行路由选择,实现路由自适应控制。同样对网络的点比和能耗等做好自适应控制,通过对预测误差的比较,从而提高其预测精度。     

ARMA模型AR部分阶与参数估计的超定快速递推算法*

本文给出了一种新的ARMA模型AR部分的阶次与参数估计的超定快速递推算法,提高了运算速度和估计精度。     

残差初值的最小二乘估计

本文给出了一种简便的残差初值算法。该法在小样本情况下，对改善最小二乘法估计参数的精度特别有效，它不但适用于ARMA模型，而且也适用于CARMA模型和MA模型。     

General results on the McMillan degree and the Kronecker indices of ARMA and MFD models

This paper shows that the McMillan degree of general ARMA and MFD models is equal to the pole-zero excess of the matrix consisting of the polynomial factors. Furthermore, the left Kronecker indices are equal to the row degrees of this matrix if and only if it is row-reduced and irreducible. For left coprime ARMA and MFD models the McMillan degree and the left Kronecker indices are related to the determinantal degree and the row degrees of a suitable submatrix of the polynomial factors. Under certain (necessary and sufficient) conditions this information can even be inferred from the denominator matrices in the ARMA and MFD models. Finally a rank test is presented for actually computing the McMillan degree of left coprime ARMA and MFD models.     

ARMA models,their Kronecker indices and their McMillan degree

This paper highlights some difficulties with the use of ARMA models with leading unit coefficient matrix in system identification. It is shown that the McMillan degree of such models is not in any easy way related to the row degrees of the polynomial factors of the ARMA model. A rank test is given for the McMillan degree of such models and it is shown that this degree will generically be a multiple of the dimension of the observation vector.     

Rank pattern-based selection of input/output ARMA model order

Rational transfer functions are standard models for radar targets and adaptive beamforming. Fitting these models essentially involves estimating the transfer function “poles and zeroes.” A key preliminary step in this estimation process is to determine the numbers of poles and zeros, or equivalently to determine the order of the corresponding ARMA model. A pattern-based method of order selection using matrix ranks is proposed for input/output (I/O) ARMA models, where ARMA model inputs and outputs are each observed in additive noise with known variances. This I/O ARMA model encompasses two distinct scenarios: observational studies in which all observations—those of both inputs and outputs—are erred, and controlled experiments in which outputs are observed with error while inputs are known without error. The proposed rank pattern method exploits the eigenvalue structure of the covariance matrices associated with the observed data and performs well for short data records at moderate SNRs.     

On the identifiability of non-Gaussian ARMA models using cumulants

A fixed set of output cumulants of order greater than two guarantees unique identification of known-order causal ARMA (autoregressive moving-average) models, which are driven by unobservable non-Gaussian i.i.d. noise. The models are allowed to be non-minimum-phase, and their outputs may be corrupted by additive colored Gaussian noise of unknown covariance. The ARMA parameters can be estimated either by means of linear equations and closed-form expressions or by minimizing quadratic cumulant matching criteria. The latter approach requires computation of cumulants in terms of the ARMA parameters, which is carried out in the state space using Kronecker products     

Symbolic ARMA Model Analysis

ARMA models provide a parsimonious and flexible mechanism for modeling the evolution of a time series. Some useful measures of these models (e.g., the autocorrelation function or the spectral density function) are tedious to compute by hand. This paper uses a computer algebra system, not simulation, to calculate measures of interest associated with ARMA models.     

An innovations approach to least-squares estimation--Part VII: Some applications of vector autoregressive-moving average models

We use the innovations method to solve some linear estimation problems for stochastic processes described as the solution of high-order linear difference equations driven by colored noise. Such models are often called vector or multivariable auto-regressive-moving average (ARMA) models. We illustrate how the use of ARMA models can provide some simplifications and some new results in the problem of state estimation in colored noise.     

ARMA canonical forms obtained from constructibility invariants

In recent approaches, multivariable ARMA models have been derived from observable canonical MFDs, but it was realized that these models have a number of serious limitations and disadvantages. This paper presents a new approach based on the idea of observing the state from past outputs, which leads to the construction of monic ARMA models defined by the constructibility invariants. The derivation and structural properties of these constructibility forms are investigated.     

应用于地震数据去卷的自校正白噪声估值器

本文把地震数据去卷问题处理为估计带观测噪声的ARMA模型的白噪声问题,应用时间 序列分析方法提出了不同于Mendel的新的稳态最优白噪声估值器,文章基于两个ARMA新 息模型的在线辨识,进一步给出了自校正白噪声估值器.     

含ARMA噪声系统模型的参数辨识方法*

实际问题中，大量的动态系统控制问题可归结为含ＭＡ，ＡＲＭＡ噪声系统模型的参数辨识问题。本文提出ＲＭＡ，ＲＡＲＭＡ两种系统模型参数辨识的一种新方法，主要手段是构造和研究特殊的辅助线性模型。理论分析和实际计算表明，本文方法较传统表度有明显提高。     

Feedback,causality and distance between arma models

The purpose of this paper is to analyze in bivariate vector autoregression the relationship between feedback in stochastic systems, Granger causality and a measure of dissimilarity between ARMA models. In particular, we consider a bivariate vector autoregressive processes of order p (a bivariate VAR(p) process) and we prove if the distance between the univariate ARMA models implied by the VAR representation is greater than a certain number that is a function of p, then Granger causality must exist in at least one direction in the variables.