oncausal spatial prediction filtering based on an ARMA model

Conventional f-x prediction filtering methods are based on an autoregressive model. The error section is first computed as a source noise but is removed as additive noise to obtain the signal, which results in an assumption inconsistency before and after filtering. In this paper, an autoregressive, moving-average model is employed to avoid the model inconsistency. Based on the ARMA model, a noncasual prediction filter is computed and a self-deconvolved projection filter is used for estimating additive noise in order to suppress random noise. The 1-D ARMA model is also extended to the 2-D spatial domain, which is the basis for noncasual spatial prediction filtering for random noise attenuation on 3-D seismic data. Synthetic and field data processing indicate this method can suppress random noise more effectively and preserve the signal simultaneously and does much better than other conventional prediction filtering methods.

Study on Optimality of Two-Stage Estimation with ARMA Model Random Bias

The optimality of two-stage state estimation with ARMA model random bias is studiedin this paper. Firstly, the optimal augmented state Kalman filter is given; Secondly, the two-stageKalman estimator is designed. Finally, under an algebraic constraint condition, the equivalencebetween the two-stage Kalman estimator and the optimal augmented state Kalman filter is proved.Thereby, the algebraic constraint conditions of optimal two-stage state estimation in the presence ofARMA model random bias are given.

ARMA Modelling for Whispered Speech

The Autoregressive Moving Average （ARMA） model for whispered speech is proposed. with normal speech, whispered speech has no fundamental frequency because of the glottis being semi-opened and turbulent flow being created, and formant shifting exists in the lower frequency region due to the narrowing of the tract in the false vocal fold regions and weak acoustic coupling with the aubglottal system. Analysis shows that the effect of the subglottal system is to introduce additional pole-zero pairs into the vocal tract transfer function. Theoretically, the method based on an ARMA process is superior to that based on an AR process in the spectral analysis of the whispered speech. Two methods, the least squared modified Yule-Walker likelihood estimate （LSMY） algorithm and the Frequency-Domain Steiglitz-Mcbide （FDSM） algorithm, are applied to the ARMA mfldel for the whispered speech. The performance evaluation shows that the ARMA model is much more appropriate for representing the whispered speech than the AR model, and the FDSM algorithm provides a name acorate estimation of the whispered speech spectral envelope than the LSMY algorithm with higher conputational complexity.

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.

Simulation of the growth ring density of Larix olgensis plantation wood with the ARMA model

Because growth ring data have temporal features, time series analysis can be used to simulate and reveal changes in the life of a tree and contribute to plantation management. In this study, the autoregressive(AR) and moving average modeling method was used to simulate the time series for growth ring density in a larch plantation with different initial planting densities. We adopted the Box–Jenkins method for the modeling, which was initially based on an intuitive analysis of sequence graphs followed by the augmented Dickey–Fuller stationarity test. The order p and q of the ARMA(p, q) model was determined based on the autocorrelation and partial correlation coefficient figure truncated on the respective order.Through the residual judgment, the model AR(2) was only fitted to the larch growth ring density series for the plantation with the 1.5 9 2.0 m^2 initial planting density.Because the residuals series for the other three series was not shown as a white noise sequence, the modeling was rerun. Larch wood from the initial planting density of2.0 9 2.0 m^2 was modeled by ARMA(2, 1), and ARMA((1, 5), 3) fitted to the 2.5 9 2.5 m^2 initial planting density,and the 3.0 9 3.0 m^2 was modeled by AR(1, 2, 5).Although the ARMA modeling can simulate the change in growth ring density, data for the different growth ring time series were described by different models. Thus, time series modeling can be suitable for growth ring data analysis, revealing the time domain and frequency domain of growth ring data.

Analysis and Prediction of Rural Residents’ Living Consumption Growth in Sichuan Province Based on Markov Prediction and ARMA Model

I select 32 samples concerning per capita living consumption of rural residents in Sichuan Province during the period 1978-2009. First, using Markov prediction method, the growth rate of living consumption level in the future is predicted to largely range from 10% to 20%. Then, in order to improve the prediction accuracy, time variable t is added into the traditional ARMA model for modeling and prediction. The prediction results show that the average relative error rate is 1.56%, and the absolute value of relative error during the period 2006-2009 is less than 0.5%. Finally, I compare the prediction results during the period 2010-2012 by Markov prediction method and ARMA model, respectively, indicating that the two are consistent in terms of growth rate of living consumption, and the prediction results are reliable. The results show that under the similar policies, rural residents' consumer demand in Sichuan Province will continue to grow in the short term, so it is necessary to further expand the consumer market.

基于ARMA模型的城乡居民收入差距预测分析--以安徽省为例

ARMA模型是当前普遍应用的时间序列建模方法之一。选取安徽省1980—2020年的城乡居民收入差距数据为样本,借助EVIEWS9.0软件,针对绝对收入差距与相对收入差距先后构建ARIMA((1,4),1,0)与ARMA(1,3)模型,两个模型的样本内静态预测结果均较好。分别利用所建立的两个模型,样本外动态预测2021—2023年安徽省城乡居民绝对收入差距依次为23756.7元、24846.8元与26094.6元,相对收入差距依次为2.563078元、2.563116元与2.563147元,以期为相关部门制定政策提供数据支持。

基于ARMA模型的海洋磁力测量数据小波去噪方法研究

海洋磁力测量是指通过安置在海底质子旋进磁力仪来直接测量地磁场,这样在海洋表面和海底同时测量,就可以得到地磁场的垂直梯度。但处理海洋磁力测量数据时不仅需要消耗大量时间,并且分块处理拼图还会导致精确度较低。为了实现对海洋地质层参数的准确测量,提出基于ARMA模型的海洋磁力测量数据小波去噪方法。构建海洋磁力测量数据的小波降噪和滤波检测模型,通过对海洋磁力测量数据的类型化分类识别,进行多波束的信息分割,实现对海洋磁力测量数据的自动滤波降噪,使得输出数据更清晰、自然。测试结果表明,使用ARMA模型后,输出数据的信噪比较之前提升了33.6632 dB,海洋磁力测量数据去噪效果好。通过ARMA模型对海洋磁力数据进行去噪,有助于达到更精确的海洋磁力测量数据,为研究地磁场及其变化、海洋地质构造、矿产预测和国防建设提供了重要支持。

基于ARMA模型的隧道变形预测及参数估计分析

以北京市海淀区某地铁站一体化棚户区改造项目为例,运用ARMA模型对高层建筑盖挖逆作法施工过程中邻近既有地铁隧道变形进行预测。以既有地铁隧道沉降实时监测数据为原始数据集,对原始数据集进行适当插补处理后,通过极大似然估计法对模型进行参数估计,给出了模型关键参数,构建了合理的预测模型。将模型预测结果与实测数据进行对比,显示预测结果与实测数据变化趋势高度吻合,充分验证了预测模型的可行性、有效性与稳定性。

基于ARMA-GARCH模型的中证绿色债券指数预测

文章选取了中证绿色债券指数2018年11月1日至2023年12月29日的日收盘价,通过Eviews10.0建立ARMA-GARCH模型预测其时间序列变化趋势并得出相应的结论,其实证结果表明:ARMA-GARCH模型可以有效地应用于绿色债券市场预测未来走势,其中静态预测结果优于动态预测,未来一定时间内绿色债券指数收盘价总体呈现向上增长趋势。这对投资者降低投资风险、挖掘投资机遇具有重要意义,便于投资者更好地制定投资策略,控制风险,进而获得更高的收益。

Wind Speed Forecasting Based on ARMA-ARCH Model in Wind Farms

Wind speed forecasting is signif icant for wind farm planning and power grid operation. The research in this paper uses Eviews software to build the ARMA (autoregressive moving average) model of wind speed time series, and employs Lagrange multipliers to test the ARCH (autoregressive conditional heteroscedasticity) effects of the residuals of the ARMA model. Also, the corresponding ARMA-ARCH models are established, and the wind speed series are forecasted by using the ARMA model and ARMA-ARCH model respectively. The comparison of the forecasting accuracy of the above two models shows that the ARMA-ARCH model possesses higher forecasting accuracy than the ARMA model and has certain practical value.

RECURSIVE METH0D FOR ARMA MODEL ESTIMATION (Ⅱ)

In this paper a new recursive method for ARMA model estimation is given. Same as in [1], theorder's estimator is strongly consistent, and the parameter's estimators defer to CLT and LILunder a natural condition. Compared with the previous metheds suggested by Hannan & Kavalieris(1984), Wang Shouren & Chen Zhaoguo (1985) and Franke (1985), this methed has some advantages:the amount of calculat on work is smaller, the minimum-phase property of coeffcient estimators canbe guaranteed,the BAN estimators for MA or AR model can be obtained directly,and the simulationshows that this method is more accurate in estimating the order and parameters.

The ARMA model’s pole characteristics of Doppler signals fromthe carotid artery and their classification application

In order to diagnose the cerebral infarction, a classification system based on the ARMA model and BP (Back-Propagation) neural network is presented to analyze blood flow Doppler signals from the carotid artery. In this system, an ARMA model is first used to analyze the audio Doppler blood flow signals from the carotid artery. Then several characteristic parameters of the pole's distribution are estimated. After studies of these characteristic parameters' sensitivity to the textcolor cerebral infarction diagnosis, a BP neural network using sensitive parameters is established to classify the normal or abnormal state of the cerebral vessel. With 474 cases used to establish the appropriate neural network, and 52 cases used to test the network, the results show that the correct classification rate of both training and testing are over 94%. Thus this system is useful to diagnose the cerebral infarction.

A CONSTRAINED LEAST SQUARES FITTING TECHNIQUE FOR ARMA MODELING

Several ARMA modeling approaches are addressed. In these methods only part of a correlation sequence is employed for estimating parameters. It is satisfying, if the given correlation sequence is of real ARMA, since an ARMA process can be completely determined by part of its correlation se -quence. But for the case of a measured correlation sequence the whole sequence may be used to reduce the effect of error on model parameter estimation. In addition, these methods now do not guarantee a nonnegative spectral estimate. In view of the above-mentioned fact, a constrained least squares fitting technique is proposed which utilizes the whole measured correlation sequence and guarantees a nonnegative spectral estimate.

Chinese speaker-recognition based on ARMA model

A Chinese speaker recognition system, which only use speech material of nasal initials and is text-independent, is presented in this paper. According to the properties of speaker's fixed nasal cavity and stable pharynx cavity when Chinese nasal initials is spoken and a few Chinese nasal initials (the total number of them is only 101 which consists of 53 Tn- and 48 n-), the spectrum parameters of zero and pole point coefficients of all Chinese nasal initials can be gotten by using ARMA model. The performance of this system for 20 speakers is as follows' The correct recognition rate (CRR) is 87.92% for each speaker to test all initials, when randomly choosing 2, 3, 4 and 5 initials in each speaker's and then averaging their spectrum to test individual template, the average' CRRs are 91.67%, 95.00%, 96.67% and 99.97% respectively.

基于ARMA-SSESM组合模型的危险品道路运输泄漏事故预测研究

为了预测危险品道路运输泄漏事故数量,以2013—2020年危险品道路运输泄漏月度事故为基础,运用时间序列理论建立自回归滑动平均(ARMA)预测模型和简单季节指数平滑法(SSESM)预测模型以及组合预测模型,对2021年1月—2021年6月的危险品道路运输泄漏事故数量进行预测,并对3种模型的预测精度进行比较。研究结果表明:组合预测模型的预测精度最佳,能够有效拟合时间序列的整体趋势。研究结果可为危险品道路运输泄漏事故预防工作提供参考。

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.

基于ARMA模型磁暴抑制的地震TEC异常分析

地震孕育的过程中会影响电离层的粒子浓度(TEC),因此可以通过TEC数据分析地震异常信息,但是TEC数据相较地震事件受太阳活动影响更大,所以以往的研究通常只能选择太阳活动较为平静的时期。受磁暴影响TEC数据无法正常反应2021年云南大理州等地的地震异常信息,因此抑制磁暴干扰就显得格外重要。在滑动四分位法的基础上提出一种基于ARMA模型的TEC数据磁暴抑制方法,通过重新拟合磁暴发生前后的TEC数据,从而代替原始数据进行分析,在一定程度上降低磁暴对地震的信息的影响,然后提出一种地震特征及其相关性的模型对TEC异常和地震事件的相关性程度进行分析。基于IGS数据分析中心提供的全球电离层电子浓度数据,分别对2021年云南大理州漾濞县6.4级地震和青海果洛州玛多县7.4级地震进行仿真分析,分别使用传统的滑动四分位法和滑动窗口法,并通过提出的相关性模型对磁暴抑制前后的特征和事件相关性结果进行对比,结果表明去除磁暴后的特征与地震相关性增加约13-18%。

基于ARMA-GARCH簇模型的碳配额价格波动特征研究

在“双碳”目标指引下,我国碳市场正处于从区域试点迈向全国统一的关键时期,如何实现地方衔接全国碳市场已经成为亟待解决的问题。以北京、上海、广东、湖北、天津和全国6个碳交易市场的碳价为研究对象,通过建立ARMA-GARCH簇模型对碳价波动问题进行量化分析。研究结果发现,6个碳市场的碳价受前期交易的影响较为显著、价格波动具有集群效应以及存在非对称性冲击效应,为此提出丰富交易主体、交易产品和完善市场定价机制的相关建议。

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.