Application of Seasonal Auto-regressive Integrated Moving Average Model in Forecasting the Incidence of Hand-foot-mouth Disease in Wuhan,China

Outbreaks of hand-foot-mouth disease（HFMD） have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average（ARIMA） model for time series analysis was designed in this study. Eighty-four-month（from January 2009 to December 2015） retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination（R^2）, normalized Bayesian Information Criterion（BIC） and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as（1,0,1）（0,1,1）12, with the largest coefficient of determination（R^2=0.743） and lowest normalized BIC（BIC=3.645） value. The residuals of the model also showed non-significant autocorrelations（P_（Box-Ljung（Q））=0.299）. The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.

A Study of Wind Statistics Through Auto-Regressive and Moving-Average (ARMA) Modeling

Statistical properties of winds near the Taichung Harbour are investigated. The 26 years'incomplete data of wind speeds, measured on an hourly basis, are used as reference. The possibility of imputation using simulated results of the Auto-Regressive (AR), Moving-Average (MA), and/ or Auto-Regressive and Moving-Average (ARMA) models is studied. Predictions of the 25-year extreme wind speeds based upon the augmented data are compared with the original series. Based upon the results, predictions of the 50- and 100-year extreme wind speeds are then made.

Parametric SNR Estimation Based on Auto-Regressive Model in AWGN Channels

Signal-to-noise ratio（SNR）estimation for signal which can be modeled by Auto-regressive（AR）process is studied in this paper.First,the conventional frequency domain method is introduced to estimate the SNR for the received signal in additive white Gauss noise（AWGN）channel.Then a parametric SNR estimation algorithm is proposed by taking advantage of the AR model information of the received signal.The simulation results show that the proposed parametric method has better performance than the conventional frequency doma in method in case of AWGN channel.

Auto-regressive模型在全国婴儿死亡率拟合中的应用

目的分析我国1991～2007年的婴儿死亡率的变化规律,探讨Auto-regressive模型在非平稳时间序列数据拟合中的适用性和有效性。方法对我国婴儿死亡率数据序列的平稳性和纯随机性进行预处理,然后利用SAS程序拟合Auto-regressive模型,并根据决定系数R2评价其拟合效果。结果我国婴儿死亡率为非平稳时间序列,总体呈现随时间线性递减的长期趋势,同时又包含一定的随机信息,采用Auto-regressive模型拟合效果较好。结论 Auto-regressive模型可以用来拟合我国婴儿死亡率的数据,并可以推广应用到卫生领域中其他具有非平稳时间序列特征的数据,为相关卫生管理部门制定策略措施提供科学的理论依据。

基于(残差)Auto-Regressive模型利用MATLAB解决经济非平稳时间序列的预测分析

利用(残差)Auto—Regressive模型对我国1978年—2005年的GDP进行建模与预测,显示出该拟合模型优于ARIMA模型,并运行MATLAB软件,实现了建模仿真的全过程,显示了MATLAB的强大科学计算与可视化功能.

Short Term Load Forecasting Using Subset Threshold Auto Regressive Model

The subset threshold auto regressive (SSTAR) model, which is capable of reproducing the limit cycle behavior of nonlinear time series, is introduced. The algorithm for fitting the sampled data with SSTAR model is proposed and applied to model and forecast power load. Numerical example verifies that desirable accuracy of short term load forecasting can be achieved by using the SSTAR model.

Parameter Estimation of Time-Varying ARMA Model

The auto-regressive moving-average (ARMA) model with time-varying parameters is analyzed. The time-varying parameters are assumed to be a linear combination of a set of basis time-varying functions, and the feedback linear estimation algorithm is used to estimate the time-varying parameters of the ARMA model. This algorithm includes 2 linear least squares estimations and a linear filter. The influence of the order of basis time-(varying) functions on parameters estimation is analyzed. The method has the advantage of simple, saving computation time and storage space. Theoretical analysis and experimental results show the validity of this method.

EXPERIMENTS WITH SHORT-TERM CLIMATE PREDICTION MODELS ON SSTA OVER THE NINO OCEANIC REGION

Predictions of averaged SST monthly anomalous series for Nino 1-4 regions in the context of auto-adaptive filter are made using a model combining the singular spectrum analysis (SSA) and auto-regression (AR). The results have shown that the scheme is efticient in forward forecaning of the strong ENSO event in 1997- 1998, it is of high reliability in retrospective forecasting of three corresponding historical strong ENSO events. It is seen that the scheme has stable skill and large accuracy for experiments of both independent samples and real cases.With modifications, the SSA-AR scheme is expected to become an efficient model in routine predictions of ENSO.

Application of deep autoencoder model for structural condition monitoring

Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion.

ARMA-GM combined forewarning model for the quality control

Three forecasting models are set up: the auto\|regressive moving average model, the grey forecasting model for the rate of qualified products P t, and the grey forecasting model for time intervals of the quality catastrophes. Then a combined forewarning system for the quality of products is established, which contains three models, judgment rules and forewarning state illustration. Finally with an example of the practical production, this modeling system is proved fairly effective.

基于VAR-LRTC-TNN的交通流量数据补全框架模型

从各类传感系统收集到的交通流数据往往会因探测器或通信故障等缘故出现数据连续性的缺失,故准确补全缺失的交通流数据对制定合理的交通管理策略至关重要。鉴于交通流数据具有低秩的特性,通过低秩张量补全模型可较好地刻画出交通流数据的全局一致性,但却无法很好地捕捉数据的局部变化趋势,一定程度上影响了效果。基于此,提出了将VAR模型和基于残差序列的LRTC-TNN模型相结合的交通流补全框架模型;采用VAR模型对缺失数据进行粗略估计,移除平均趋势,利用LRTC-TNN模型对残差时间序列进行补全,再将平均趋势还原,从而完成对交通流量数据的高精度补全;该方法不仅保留了交通流数据的全局结构,还考虑了数据局部变化的特征。研究结果表明:与基于原始交通流量数据的填充方法相比,该模型框架对单传感器和多传感器数据的连续性缺失均具有更高的补全精度。

基于SSA-Hurst-ARIMA组合模型的船舶柴油发电机组故障特征短期预测

为提高船舶柴油发电机组故障特征短期预测精度,建立基于奇异谱分析(Singular Spectrum Analysis,SSA)、Hurst指数、自回归移动平均(Auto-Regressive Integrated Moving Average,ARIMA)的组合预测模型。以某试验中船舶柴油发电机组运行数据为基础,选取增压器滑油压强数据,对比分析单一ARIMA模型、SSA主成分-ARIMA组合模型和SSA-Hurst-ARIMA组合模型的预测效果。结果表明,SSA-Hurst-ARIMA组合模型的预测效果优于单一ARIMA模型和SSA主成分-ARIMA组合模型,更适合应用于船舶柴油发电机组故障特征的短期预测。

Auto-Regressive Models of Non-Stationary Time Series with Finite Length

To analyze and simulate non-stationary time series with finite length, the statistical characteris- tics and auto-regressive (AR) models of non-stationary time series with finite length are discussed and stud- ied. A new AR model called the time varying parameter AR model is proposed for solution of non-stationary time series with finite length. The auto-covariances of time series simulated by means of several AR models are analyzed. The result shows that the new AR model can be used to simulate and generate a new time series with the auto-covariance same as the original time series. The size curves of cocoon filaments re- garded as non-stationary time series with finite length are experimentally simulated. The simulation results are significantly better than those obtained so far, and illustrate the availability of the time varying parameter AR model. The results are useful for analyzing and simulating non-stationary time series with finite length.

A novel approach to equipment health management based on auto-regressive hidden semi-Markov model(AR-HSMM)

As a new maintenance method, CBM （condition based maintenance） is becoming more and more important for the health management of complicated and costly equipment. A prerequisite to widespread deployment of CBM technology and prac- tice in industry is effective diagnostics and prognostics. Recently, a pattern recog- nition technique called HMM （hidden Markov model） was widely used in many fields. However, due to some unrealistic assumptions, diagnositic results from HMM were not so good, and it was difficult to use HMM directly for prognosis. By relaxing the unrealistic assumptions in HMM, this paper presents a novel approach to equip- ment health management based on auto-regressive hidden semi-Markov model （AR-HSMM）. Compared with HMM, AR-HSMM has three advantages： 1） It allows explicitly modeling the time duration of the hidden states and therefore is capable of prognosis. 2） It can relax observations＇ independence assumption by accom- modating a link between consecutive observations. 3） It does not follow the unre- alistic Markov chain＇s memoryless assumption and therefore provides more pow- erful modeling and analysis capability for real problems. To facilitate the computa- tion in the proposed AR-HSMM-based diagnostics and prognostics, new forward- backward variables are defined and a modified forward-backward algorithm is de- veloped. The evaluation of the proposed methodology was carried out through a real world application case study： health diagnosis and prognosis of hydraulic pumps in Caterpillar Inc. The testing results show that the proposed new approach based on AR-HSMM is effective and can provide useful support for the decision- making in equipment health management.

China's Energy Consumption Forecasting by GMDH Based Auto-Regressive Model

It is very significant for us to predict future energy consumption accurately. As for China's energy consumption annual time series, the sample size is relatively small. This paper combines the traditional auto-regressive model with group method of data handling(GMDH) suitable for small sample prediction, and proposes a novel GMDH based auto-regressive(GAR) model. This model can finish the modeling process in self-organized manner, including finding the optimal complexity model, determining the optimal auto-regressive order and estimating model parameters. Further, four different external criteria are proposed and the corresponding four GAR models are constructed. The authors conduct empirical analysis on three energy consumption time series, including the total energy consumption, the total petroleum consumption and the total gas consumption. The results show that AS-GAR model has the best forecasting performance among the four GAR models, and it outperforms ARIMA model, BP neural network model, support vector regression model and GM(1, 1) model.Finally, the authors give the out of sample prediction of China's energy consumption from 2014 to 2020 by AS-GAR model.

Time-varying parameter auto-regressive models for autocovariance nonstationary time series

In this paper, autocovariance nonstationary time series is clearly defined on a family of time series. We propose three types of TVPAR (time-varying parameter auto-regressive) models: the full order TVPAR model, the time-unvarying order TVPAR model and the time-varying order TV-PAR model for autocovariance nonstationary time series. Related minimum AIC (Akaike information criterion) estimations are carried out.

LS-SVM回归算法在刀具磨损量预测中的应用

提出了基于最小二乘支持向量机回归算法的刀具磨损量预测方法。该方法首先利用经验模态分解算法对非线性、非平稳的声发射信号进行平稳化处理,得到了若干个固有模态函数;然后建立了每个固有模态函数的自回归模型,并提取模型系数构造特征向量;最后采用最小二乘支持向量机回归算法实现了刀具磨损量的预测。该方法与神经网络预测算法相比,具有更高的预测准确率,可有效预测当前切削状态下10s后的刀具磨损量。

基于异方差检验的水文过程隐含周期分析模型及其应用--Ⅰ：模型

本文在讨论水文时序平稳性和异方差性的基础上,提出了检测水文时序隐含周期的模型,即对水文时序设计对数变换序列,提高序列的平稳性;建立相应的自回归模型降低残差的异方差性;通过对所得平稳序列进行周期图分析确定可能的主周期分量。针对水文时序的特性提出包括具有确定频率的正弦项存在性检验、具有确定整数周期的非正弦周期项存在性检验和非确定频率的隐含周期性检验(Fisher检验)三种检验方式,可择其中一种确定主周期的显著水平。采用该模型可以解决利用自相关函数判断序列周期性所不能解决的问题,可进一步挖掘数据隐含的、深层次信息。最后,本文还编制了水文时序隐含周期的计算流程。在理论上,该模型不仅解决了隐含周期的检测问题,对于有确定周期的序列亦适用。

基于小波包-AR谱的变速器轴承故障特征提取

提出了一种小波包-AR谱估计和计算散度相结合的汽车变速器轴承故障特征提取方法。将6种不同磨损状况下的变速器轴承振动信号进行小波包分解,重构各频段信号并进行自回归(auto regressive,简称AR)谱估计,最后计算各故障轴承到新轴承之间的散度值。试验结果表明,不论是轴承的轴向间隙,还是径向间隙差异及疲劳剥落,在小波包-AR谱的谱图上均有明显的反映,该方法可以有效提取出汽车变速器轴承振动信号中的故障特征。

应用EMD-AR谱提取柴油机曲轴轴承故障特征

提出了一种基于经验模式分解(EMD)和AR(auto regressive)谱技术相结合的曲轴轴承磨损故障诊断的新方法。利用EMD方法分解发动机非稳态加速振动信号,得到一系列平稳的本征模式函数(IMF)分量,对占信号能量主要部分的前5阶IMF分量进行AR谱估计,分析各IMF分量的AR谱频带能量,提取能够反映曲轴轴承磨损故障的IMF分量的AR谱频带能量作为故障特征参数。试验时设置6组不同的振动传感器放置部位和4组不同的采集器触发转速,并利用本文提出的方法分析采集到的发动机非稳态振动信号。分析结果表明,基于EMD及AR谱技术提取得到的故障特征能够准确反映曲轴轴承的磨损状态,且当发动机转速高于1300 r/min,传感器放置于缸体与油底结合部右侧时,提取的故障特征最明显。