-In this paper, monthly mean SST data in a large area are used. After the spacial average of the data is carried out and the secular monthly means are substracted, a time series (Jan. 1951-Dec. 1985) of SST anomalies ...-In this paper, monthly mean SST data in a large area are used. After the spacial average of the data is carried out and the secular monthly means are substracted, a time series (Jan. 1951-Dec. 1985) of SST anomalies of the cold tongue water area in the eastern tropical Pacific Ocean is obtained. On the basis of the time series, an autoregression model, a self-exciting threshold autoregression model and an open loop autoregression model are developed respectively. The interannual variations are simulated by means of those models. The simulation results show that all the three models have made very good hindcasting for the nine El Nino events since 1951. In order to test the reliability of the open loop threshold model, extrapolated forecast was made for the period of Jan. 1986-Feb. 1987. It can be seen from the forecasting that the model could forecast well the beginning and strengthening stages of the recent El Nino event (1986-1987). Correlation coefficients of the estimations to observations are respectively 0. 84, 0. 88 and 0. 89. It is obvious that all the models work well and the open loop threshold one is the best. So the open loop threshold autoregression model is a useful tool for monitoring the SSTinterannual variation of the cold tongue water area in the Eastern Equatorial Pacific Ocean and for estimating the El Nino strength.展开更多
We consider a two-regime threshold autoregressive model where the driving noises are sequences of independent and identically distributed random variables with common distribution function which belongs to the domain ...We consider a two-regime threshold autoregressive model where the driving noises are sequences of independent and identically distributed random variables with common distribution function which belongs to the domain of attraction of double exponential distribution. If in addition, for each and where denotes the convolution of the distribution function and we determine the tail behavior of the process and give the exact values of the coefficient.展开更多
When linear regressive models such as AR or ARMA model are used for fitting and predicting climatic time series,results are often not sufficiently good because nonlinear variations in the time series.In this paper, a ...When linear regressive models such as AR or ARMA model are used for fitting and predicting climatic time series,results are often not sufficiently good because nonlinear variations in the time series.In this paper, a nonlinear self-exciting threshold autoregressive(SETAR)model is applied to modeling and predicting the time series of flood/drought runs in Beijing,which were derived from the graded historical flood/drought records in the last 511 years(1470—1980).The results show that the modeling and predicting with the SETAR model are much better than that of the AR model.The latter can predict the flood/drought runs with a length only less than two years,while the formal can predict more than three-year length runs.This may be due to the fact that the SETAR model can renew the model according to the run-turning points in the process of predic- tion,though the time series is nonstationary.展开更多
This paper represented Autoregressive Neural Network (ARNN) and meant threshold methods for recognizing eye movements for control of an electrical wheelchair using EEG technology. The eye movements such as eyes open, ...This paper represented Autoregressive Neural Network (ARNN) and meant threshold methods for recognizing eye movements for control of an electrical wheelchair using EEG technology. The eye movements such as eyes open, eyes blinks, glancing left and glancing right related to a few areas of human brain were investigated. A Hamming low pass filter was applied to remove noise and artifacts of the eye signals and to extract the frequency range of the measured signals. An autoregressive model was employed to produce coefficients containing features of the EEG eye signals. The coefficients obtained were inserted the input layer of a neural network model to classify the eye activities. In addition, a mean threshold algorithm was employed for classifying eye movements. Two methods were compared to find the better one for applying in the wheelchair control to follow users to reach the desired direction. Experimental results of controlling the wheelchair in the indoor environment illustrated the effectiveness of the proposed approaches.展开更多
The development of Ivorian public debt in recent years has raised concerns.Is its current level capable of boosting the economy or,on the contrary,being at the source of a recession?This paper analyzes the effect of t...The development of Ivorian public debt in recent years has raised concerns.Is its current level capable of boosting the economy or,on the contrary,being at the source of a recession?This paper analyzes the effect of the level of indebtedness on economic growth in Côte d’Ivoire using the Threshold Autoregressive(TAR)model over the period 1970-2018.The results obtained in the short run shed light on the no relationship between public debt and economic growth.In the long run,on the other hand,there is a bi-directional granger causality between public debt and the sustainability of economic growth.The non-linearity between the variables of interest has been studied and the results show the presence of a threshold effect:beyond 48.03 percent of GDP,any increase in public debt by 1%should reduce economic growth by 0.28%.Thus,the study questions the relevance of the criterion set by the WAEMU:public debt<70%of GDP.展开更多
A new prediction method based on the nonlinear autoregressive model is proposed to improve the accuracy of medium-term and long-term predictions of Satellite Clock Bias(SCB).Forecast experiments for three time periods...A new prediction method based on the nonlinear autoregressive model is proposed to improve the accuracy of medium-term and long-term predictions of Satellite Clock Bias(SCB).Forecast experiments for three time periods were implemented based on the precision SCB published on the International GNSS Server(IGS)server.The results show that the medium-term and long-term prediction accuracy of the proposed approach is significantly better compared to other traditional models,with the training time being much shorter than the wavelet neural network model.展开更多
文摘-In this paper, monthly mean SST data in a large area are used. After the spacial average of the data is carried out and the secular monthly means are substracted, a time series (Jan. 1951-Dec. 1985) of SST anomalies of the cold tongue water area in the eastern tropical Pacific Ocean is obtained. On the basis of the time series, an autoregression model, a self-exciting threshold autoregression model and an open loop autoregression model are developed respectively. The interannual variations are simulated by means of those models. The simulation results show that all the three models have made very good hindcasting for the nine El Nino events since 1951. In order to test the reliability of the open loop threshold model, extrapolated forecast was made for the period of Jan. 1986-Feb. 1987. It can be seen from the forecasting that the model could forecast well the beginning and strengthening stages of the recent El Nino event (1986-1987). Correlation coefficients of the estimations to observations are respectively 0. 84, 0. 88 and 0. 89. It is obvious that all the models work well and the open loop threshold one is the best. So the open loop threshold autoregression model is a useful tool for monitoring the SSTinterannual variation of the cold tongue water area in the Eastern Equatorial Pacific Ocean and for estimating the El Nino strength.
文摘We consider a two-regime threshold autoregressive model where the driving noises are sequences of independent and identically distributed random variables with common distribution function which belongs to the domain of attraction of double exponential distribution. If in addition, for each and where denotes the convolution of the distribution function and we determine the tail behavior of the process and give the exact values of the coefficient.
文摘针对非线性波动性发展的滑坡,为了提高其位移变化的预测精度,以经验模态分解(Empirical Mode Decomposition)方法对滑坡监测地表位移的时间序列进行处理,将不规律变化的位移序列转化为存在一定规律变化的模态分量,得到不同频率的位移分量,对每一分量单独预测,避免误差相互影响,通过预测所有分量的变化趋势来综合预测位移序列的变化趋势,利用改进门限自回归模型(Threshold Auto Regressive)对非稳态谐波描述性较好的优势预测滑坡位移分量,最后模态叠加得到最终预测位移,建立了基于经验模态分解和门限自回归模型的组合预测模型,结合白水河滑坡实例数据验证该模型的预测精度,通过与BP神经网络模型、长短时间记忆网络模型进行预测对比,提出的组合模型预测精度较高,为滑坡位移的预测提供了一种新的方法。
文摘When linear regressive models such as AR or ARMA model are used for fitting and predicting climatic time series,results are often not sufficiently good because nonlinear variations in the time series.In this paper, a nonlinear self-exciting threshold autoregressive(SETAR)model is applied to modeling and predicting the time series of flood/drought runs in Beijing,which were derived from the graded historical flood/drought records in the last 511 years(1470—1980).The results show that the modeling and predicting with the SETAR model are much better than that of the AR model.The latter can predict the flood/drought runs with a length only less than two years,while the formal can predict more than three-year length runs.This may be due to the fact that the SETAR model can renew the model according to the run-turning points in the process of predic- tion,though the time series is nonstationary.
文摘This paper represented Autoregressive Neural Network (ARNN) and meant threshold methods for recognizing eye movements for control of an electrical wheelchair using EEG technology. The eye movements such as eyes open, eyes blinks, glancing left and glancing right related to a few areas of human brain were investigated. A Hamming low pass filter was applied to remove noise and artifacts of the eye signals and to extract the frequency range of the measured signals. An autoregressive model was employed to produce coefficients containing features of the EEG eye signals. The coefficients obtained were inserted the input layer of a neural network model to classify the eye activities. In addition, a mean threshold algorithm was employed for classifying eye movements. Two methods were compared to find the better one for applying in the wheelchair control to follow users to reach the desired direction. Experimental results of controlling the wheelchair in the indoor environment illustrated the effectiveness of the proposed approaches.
文摘The development of Ivorian public debt in recent years has raised concerns.Is its current level capable of boosting the economy or,on the contrary,being at the source of a recession?This paper analyzes the effect of the level of indebtedness on economic growth in Côte d’Ivoire using the Threshold Autoregressive(TAR)model over the period 1970-2018.The results obtained in the short run shed light on the no relationship between public debt and economic growth.In the long run,on the other hand,there is a bi-directional granger causality between public debt and the sustainability of economic growth.The non-linearity between the variables of interest has been studied and the results show the presence of a threshold effect:beyond 48.03 percent of GDP,any increase in public debt by 1%should reduce economic growth by 0.28%.Thus,the study questions the relevance of the criterion set by the WAEMU:public debt<70%of GDP.
基金2022 Basic Scientific Research Project supported by Liaoning Provincial Education Department(No.LJKMZ20221686)。
文摘A new prediction method based on the nonlinear autoregressive model is proposed to improve the accuracy of medium-term and long-term predictions of Satellite Clock Bias(SCB).Forecast experiments for three time periods were implemented based on the precision SCB published on the International GNSS Server(IGS)server.The results show that the medium-term and long-term prediction accuracy of the proposed approach is significantly better compared to other traditional models,with the training time being much shorter than the wavelet neural network model.
