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 assu...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.展开更多
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. Final...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.展开更多
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 cr...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.展开更多
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 feedbac...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.展开更多
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...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.展开更多
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 th...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.展开更多
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...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.展开更多
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 conditio...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.展开更多
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 thi...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.展开更多
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...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.展开更多
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 ph...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.展开更多
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 simu...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.展开更多
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 cata...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.展开更多
基金This research was financially supported by National Natural Science Foundation of China （Grant No. 40604016） and the National Hi-Tech Research and Development Program （863 Program） （Grants No. 2006AA09A102-09 and No. 2007AA06Z229）.
文摘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.
文摘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.
基金supported by the Independent Innovation Foundation of Shandong University(No.2009JC004)the Natural Science Foundation of Shandong Province(No.Y2007G31)
文摘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.
文摘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.
基金financially supported by the National Sci-Tech Support Plan of China(Grant No.2015BAD14B05)
文摘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.
文摘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.
文摘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.
文摘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.
基金This work was supported by the KeyTeacherFundsofEducationMinistryofChina.
文摘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.
文摘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.
文摘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.
基金The project is partly supported by the National Science Council, Contract Nos. NSC-89-261 l-E-019-024 (JZY), and NSC-89-2611-E-019-027 (CRC).
文摘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.
文摘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.
