The paper not only studies the noise reduction methods of chaotic time series with noise and its reconstruction techniques, but also discusses prediction techniques of chaotic time series and its applications based on...The paper not only studies the noise reduction methods of chaotic time series with noise and its reconstruction techniques, but also discusses prediction techniques of chaotic time series and its applications based on chaotic data noise reduction. In the paper, we first decompose the phase space of chaotic time series to range space and null noise space. Secondly we restructure original chaotic time series in range space. Lastly on the basis of the above, we establish order of the nonlinear model and make use of the nonlinear model to predict some research. The result indicates that the nonlinear model has very strong ability of approximation function, and Chaos predict method has certain tutorial significance to the practical problems.展开更多
COVID-19 comes from a large family of viruses identied in 1965;to date,seven groups have been recorded which have been found to affect humans.In the healthcare industry,there is much evidence that Al or machine learni...COVID-19 comes from a large family of viruses identied in 1965;to date,seven groups have been recorded which have been found to affect humans.In the healthcare industry,there is much evidence that Al or machine learning algorithms can provide effective models that solve problems in order to predict conrmed cases,recovered cases,and deaths.Many researchers and scientists in the eld of machine learning are also involved in solving this dilemma,seeking to understand the patterns and characteristics of virus attacks,so scientists may make the right decisions and take specic actions.Furthermore,many models have been considered to predict the Coronavirus outbreak,such as the retro prediction model,pandemic Kaplan’s model,and the neural forecasting model.Other research has used the time series-dependent face book prophet model for COVID-19 prediction in India’s various countries.Thus,we proposed a prediction and analysis model to predict COVID-19 in Saudi Arabia.The time series dependent face book prophet model is used to t the data and provide future predictions.This study aimed to determine the pandemic prediction of COVID-19 in Saudi Arabia,using the Time Series Analysis to observe and predict the coronavirus pandemic’s spread daily or weekly.We found that the proposed model has a low ability to forecast the recovered cases of the COVID-19 dataset.In contrast,the proposed model of death cases has a high ability to forecast the COVID-19 dataset.Finally,obtaining more data could empower the model for further validation.展开更多
Building the prediction model(s) from the historical time series has attracted many researchers in last few decades. For example, the traders of hedge funds and experts in agriculture are demanding the precise models ...Building the prediction model(s) from the historical time series has attracted many researchers in last few decades. For example, the traders of hedge funds and experts in agriculture are demanding the precise models to make the prediction of the possible trends and cycles. Even though many statistical or machine learning (ML) models have been proposed, however, there are no universal solutions available to resolve such particular problem. In this paper, the powerful forward-backward non-linear filter and wavelet-based denoising method are introduced to remove the high level of noise embedded in financial time series. With the filtered time series, the statistical model known as autoregression is utilized to model the historical times aeries and make the prediction. The proposed models and approaches have been evaluated using the sample time series, and the experimental results have proved that the proposed approaches are able to make the precise prediction very efficiently and effectively.展开更多
A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorith...A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.展开更多
Based on discussion on the theories of support vector machines (SVM), an one-step prediction model for time series prediction is presented, wherein the chaos theory is incorporated. Chaotic character of the time ser...Based on discussion on the theories of support vector machines (SVM), an one-step prediction model for time series prediction is presented, wherein the chaos theory is incorporated. Chaotic character of the time series is taken into account in the prediction procedure; parameters of reconstruction-detay and embedding-dimension for phase-space reconstruction are calculated in light of mutual-information and false-nearest-neighbor method, respectively. Precision and functionality have been demonstrated by the experimental results on the basis of the prediction of Lorenz chaotic time series.展开更多
We put forward a chaotic estimating model, by using the parameter of the chaotic system, sensitivity of the parameter to inching and control the disturbance of the system, and estimated the parameter of the model by u...We put forward a chaotic estimating model, by using the parameter of the chaotic system, sensitivity of the parameter to inching and control the disturbance of the system, and estimated the parameter of the model by using the best update option. In the end, we forecast the intending series value in its mutually space. The example shows that it can increase the precision in the estimated process by selecting the best model steps. It not only conquer the abuse of using detention inlay technology alone, but also decrease blindness of using forecast error to decide the input model directly, and the result of it is better than the method of statistics and other series means. Key words chaotic time series - parameter identification - optimal prediction model - improved change ruler method CLC number TP 273 Foundation item: Supported by the National Natural Science Foundation of China (60373062)Biography: JIANG Wei-jin (1964-), male, Professor, research direction: intelligent compute and the theory methods of distributed data processing in complex system, and the theory of software.展开更多
In order to improve the performance degradation prediction accuracy of proton exchange membrane fuel cell(PEMFC),a fusion prediction method(CKDG)based on adaptive noise complete ensemble empirical mode decomposition(C...In order to improve the performance degradation prediction accuracy of proton exchange membrane fuel cell(PEMFC),a fusion prediction method(CKDG)based on adaptive noise complete ensemble empirical mode decomposition(CEEMDAN),kernel principal component analysis(KPCA)and dual attention mechanism gated recurrent unit neural network(DA-GRU)was proposed.CEEMDAN and KPCA were used to extract the input feature data sequence,reduce the influence of random factors,and capture essential feature components to reduce the model complexity.The DA-GRU network helps to learn the feature mapping relationship of data in long time series and predict the changing trend of performance degradation data more accurately.The actual aging experimental data verify the performance of the CKDG method.The results show that under the steady-state condition of 20%training data prediction,the CKDA method can reduce the root mean square error(RMSE)by 52.7%and 34.6%,respectively,compared with the traditional LSTM and GRU neural networks.Compared with the simple DA-GRU network,RMSE is reduced by 15%,and the degree of over-fitting is reduced,which has higher accuracy.It also shows excellent prediction performance under the dynamic condition data set and has good universality.展开更多
The prediction methods and its applications of the nonlinear dynamic systems determined from chaotic time series of low-dimension are discussed mainly. Based on the work of the foreign researchers, the chaotic time se...The prediction methods and its applications of the nonlinear dynamic systems determined from chaotic time series of low-dimension are discussed mainly. Based on the work of the foreign researchers, the chaotic time series in the phase space adopting one kind of nonlinear chaotic model were reconstructed. At first, the model parameters were estimated by using the improved least square method. Then as the precision was satisfied, the optimization method was used to estimate these parameters. At the end by using the obtained chaotic model, the future data of the chaotic time series in the phase space was predicted. Some representative experimental examples were analyzed to testify the models and the algorithms developed in this paper. ne results show that if the algorithms developed here are adopted, the parameters of the corresponding chaotic model will be easily calculated well and true. Predictions of chaotic series in phase space make the traditional methods change from outer iteration to interpolations. And if the optimal model rank is chosen, the prediction precision will increase notably. Long term superior predictability of nonlinear chaotic models is proved to be irrational and unreasonable.展开更多
Time series prediction has been successfully used in several application areas, such as meteoro-logical forecasting, market prediction, network traffic forecasting, etc. , and a number of techniques have been develop...Time series prediction has been successfully used in several application areas, such as meteoro-logical forecasting, market prediction, network traffic forecasting, etc. , and a number of techniques have been developed for modeling and predicting time series. In the traditional exponential smoothing method, a fixed weight is assigned to data history, and the trend changes of time series are ignored. In this paper, an uncertainty reasoning method, based on cloud model, is employed in time series prediction, which uses cloud logic controller to adjust the smoothing coefficient of the simple exponential smoothing method dynamically to fit the current trend of the time series. The validity of this solution was proved by experiments on various data sets.展开更多
The contribution of this work is twofold: (1) a multimodality prediction method of chaotic time series with the Gaussian process mixture (GPM) model is proposed, which employs a divide and conquer strategy. It au...The contribution of this work is twofold: (1) a multimodality prediction method of chaotic time series with the Gaussian process mixture (GPM) model is proposed, which employs a divide and conquer strategy. It automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. (2) An effective sparse hard-cut expec- tation maximization (SHC-EM) learning algorithm for the GPM model is proposed to improve the prediction performance. SHO-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning based on these pseudo inputs. Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction, but also the prediction confidence interval SHC-EM outperforms the traditional variational 1earning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning.展开更多
In order to effectively analyse the multivariate time series data of complex process,a generic reconstruction technology based on reduction theory of rough sets was proposed,Firstly,the phase space of multivariate tim...In order to effectively analyse the multivariate time series data of complex process,a generic reconstruction technology based on reduction theory of rough sets was proposed,Firstly,the phase space of multivariate time series was originally reconstructed by a classical reconstruction technology.Then,the original decision-table of rough set theory was set up according to the embedding dimensions and time-delays of the original reconstruction phase space,and the rough set reduction was used to delete the redundant dimensions and irrelevant variables and to reconstruct the generic phase space,Finally,the input vectors for the prediction of multivariate time series were extracted according to generic reconstruction results to identify the parameters of prediction model.Verification results show that the developed reconstruction method leads to better generalization ability for the prediction model and it is feasible and worthwhile for application.展开更多
This paper presents some nonlinear models for time series. The structures and training methods for each model have been analyzed and studied. Experimental results for some common time series are given.
The prediction methods for nonlinear dynamic systems which are decided by chaotic time series are mainly studied as well as structures of nonlinear self-related chaotic models and their dimensions. By combining neural...The prediction methods for nonlinear dynamic systems which are decided by chaotic time series are mainly studied as well as structures of nonlinear self-related chaotic models and their dimensions. By combining neural networks and wavelet theories, the structures of wavelet transform neural networks were studied and also a wavelet neural networks learning method was given. Based on wavelet networks, a new method for parameter identification was suggested, which can be used selectively to extract different scales of frequency and time in time series in order to realize prediction of tendencies or details of original time series. Through pre-treatment and comparison of results before and after the treatment, several useful conclusions are reached: High accurate identification can be guaranteed by applying wavelet networks to identify parameters of self-related chaotic models and more valid prediction of the chaotic time series including noise can be achieved accordingly.展开更多
Water level prediction of river runoff is an important part of hydrological forecasting.The change of water level not only has the trend and seasonal characteristics,but also contains the noise factors.And the water l...Water level prediction of river runoff is an important part of hydrological forecasting.The change of water level not only has the trend and seasonal characteristics,but also contains the noise factors.And the water level prediction ability of a single model is limited.Since the traditional ARIMA(Autoregressive Integrated Moving Average)model is not accurate enough to predict nonlinear time series,and the WNN(Wavelet Neural Network)model requires a large training set,we proposed a new combined neural network prediction model which combines the WNN model with the ARIMA model on the basis of wavelet decomposition.The combined model fit the wavelet transform sequences whose frequency are high with the WNN,and the scale transform sequence which has low frequency is fitted by the ARIMA model,and then the prediction results of the above are reconstructed by wavelet transform.The daily average water level data of the Liuhe hydrological station in the Chu River Basin of Nanjing are used to forecast the average water level of one day ahead.The combined model is compared with other single models with MATLAB,and the experimental results show that the accuracy of the combined model is improved by 7%compared with the traditional wavelet network under the appropriate wavelet decomposition function and the combined model parameters.展开更多
地心运动会影响地球参考框架原点的准确性,是地球参考框架进行非线性维持必须考虑的因素之一,因此提出对地心运动进行多尺度的建模和预测,以实现毫米级地球参考框架的建立和维持。采用网平移法计算的地心运动、全球地球物理流体中心(glo...地心运动会影响地球参考框架原点的准确性,是地球参考框架进行非线性维持必须考虑的因素之一,因此提出对地心运动进行多尺度的建模和预测,以实现毫米级地球参考框架的建立和维持。采用网平移法计算的地心运动、全球地球物理流体中心(global geophysical fluids center,GGFC)和国际GNSS服务(international gnss service,IGS)第三次重处理(IGSR03)提供的3组地心运动数据,首先对其一致性和差异进行了分析,然后分别利用谐波模型和Diff-LSTM模型对地心运动进行了长期和短期的建模与预测,结果显示,GGFC地心运动的预测精度优于1.5 mm,而Diff-LSTM模型的地心运动预测结果在短期内优于谐波模型,当预测步长为17时,GGFC和IGSR03的地心运动预测精度均能达到甚至优于1 mm。表明地心运动的预测精度能够满足基于地球质量中心(center of mass of the total earth system,CM)的瞬时地球参考框架的建立与维持。展开更多
基金Project supported by the National Natural Science Foundation of China(Nos.70271071,19990510,D0200201)
文摘The paper not only studies the noise reduction methods of chaotic time series with noise and its reconstruction techniques, but also discusses prediction techniques of chaotic time series and its applications based on chaotic data noise reduction. In the paper, we first decompose the phase space of chaotic time series to range space and null noise space. Secondly we restructure original chaotic time series in range space. Lastly on the basis of the above, we establish order of the nonlinear model and make use of the nonlinear model to predict some research. The result indicates that the nonlinear model has very strong ability of approximation function, and Chaos predict method has certain tutorial significance to the practical problems.
文摘COVID-19 comes from a large family of viruses identied in 1965;to date,seven groups have been recorded which have been found to affect humans.In the healthcare industry,there is much evidence that Al or machine learning algorithms can provide effective models that solve problems in order to predict conrmed cases,recovered cases,and deaths.Many researchers and scientists in the eld of machine learning are also involved in solving this dilemma,seeking to understand the patterns and characteristics of virus attacks,so scientists may make the right decisions and take specic actions.Furthermore,many models have been considered to predict the Coronavirus outbreak,such as the retro prediction model,pandemic Kaplan’s model,and the neural forecasting model.Other research has used the time series-dependent face book prophet model for COVID-19 prediction in India’s various countries.Thus,we proposed a prediction and analysis model to predict COVID-19 in Saudi Arabia.The time series dependent face book prophet model is used to t the data and provide future predictions.This study aimed to determine the pandemic prediction of COVID-19 in Saudi Arabia,using the Time Series Analysis to observe and predict the coronavirus pandemic’s spread daily or weekly.We found that the proposed model has a low ability to forecast the recovered cases of the COVID-19 dataset.In contrast,the proposed model of death cases has a high ability to forecast the COVID-19 dataset.Finally,obtaining more data could empower the model for further validation.
文摘Building the prediction model(s) from the historical time series has attracted many researchers in last few decades. For example, the traders of hedge funds and experts in agriculture are demanding the precise models to make the prediction of the possible trends and cycles. Even though many statistical or machine learning (ML) models have been proposed, however, there are no universal solutions available to resolve such particular problem. In this paper, the powerful forward-backward non-linear filter and wavelet-based denoising method are introduced to remove the high level of noise embedded in financial time series. With the filtered time series, the statistical model known as autoregression is utilized to model the historical times aeries and make the prediction. The proposed models and approaches have been evaluated using the sample time series, and the experimental results have proved that the proposed approaches are able to make the precise prediction very efficiently and effectively.
文摘A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.
文摘Based on discussion on the theories of support vector machines (SVM), an one-step prediction model for time series prediction is presented, wherein the chaos theory is incorporated. Chaotic character of the time series is taken into account in the prediction procedure; parameters of reconstruction-detay and embedding-dimension for phase-space reconstruction are calculated in light of mutual-information and false-nearest-neighbor method, respectively. Precision and functionality have been demonstrated by the experimental results on the basis of the prediction of Lorenz chaotic time series.
文摘We put forward a chaotic estimating model, by using the parameter of the chaotic system, sensitivity of the parameter to inching and control the disturbance of the system, and estimated the parameter of the model by using the best update option. In the end, we forecast the intending series value in its mutually space. The example shows that it can increase the precision in the estimated process by selecting the best model steps. It not only conquer the abuse of using detention inlay technology alone, but also decrease blindness of using forecast error to decide the input model directly, and the result of it is better than the method of statistics and other series means. Key words chaotic time series - parameter identification - optimal prediction model - improved change ruler method CLC number TP 273 Foundation item: Supported by the National Natural Science Foundation of China (60373062)Biography: JIANG Wei-jin (1964-), male, Professor, research direction: intelligent compute and the theory methods of distributed data processing in complex system, and the theory of software.
基金funded by Shaanxi Province Key Industrial Chain Project(2023-ZDLGY-24)Industrialization Project of Shaanxi Provincial Education Department(21JC018)+1 种基金Shaanxi Province Key Research and Development Program(2021ZDLGY13-02)the Open Foundation of State Key Laboratory for Advanced Metals and Materials(2022-Z01).
文摘In order to improve the performance degradation prediction accuracy of proton exchange membrane fuel cell(PEMFC),a fusion prediction method(CKDG)based on adaptive noise complete ensemble empirical mode decomposition(CEEMDAN),kernel principal component analysis(KPCA)and dual attention mechanism gated recurrent unit neural network(DA-GRU)was proposed.CEEMDAN and KPCA were used to extract the input feature data sequence,reduce the influence of random factors,and capture essential feature components to reduce the model complexity.The DA-GRU network helps to learn the feature mapping relationship of data in long time series and predict the changing trend of performance degradation data more accurately.The actual aging experimental data verify the performance of the CKDG method.The results show that under the steady-state condition of 20%training data prediction,the CKDA method can reduce the root mean square error(RMSE)by 52.7%and 34.6%,respectively,compared with the traditional LSTM and GRU neural networks.Compared with the simple DA-GRU network,RMSE is reduced by 15%,and the degree of over-fitting is reduced,which has higher accuracy.It also shows excellent prediction performance under the dynamic condition data set and has good universality.
文摘The prediction methods and its applications of the nonlinear dynamic systems determined from chaotic time series of low-dimension are discussed mainly. Based on the work of the foreign researchers, the chaotic time series in the phase space adopting one kind of nonlinear chaotic model were reconstructed. At first, the model parameters were estimated by using the improved least square method. Then as the precision was satisfied, the optimization method was used to estimate these parameters. At the end by using the obtained chaotic model, the future data of the chaotic time series in the phase space was predicted. Some representative experimental examples were analyzed to testify the models and the algorithms developed in this paper. ne results show that if the algorithms developed here are adopted, the parameters of the corresponding chaotic model will be easily calculated well and true. Predictions of chaotic series in phase space make the traditional methods change from outer iteration to interpolations. And if the optimal model rank is chosen, the prediction precision will increase notably. Long term superior predictability of nonlinear chaotic models is proved to be irrational and unreasonable.
文摘Time series prediction has been successfully used in several application areas, such as meteoro-logical forecasting, market prediction, network traffic forecasting, etc. , and a number of techniques have been developed for modeling and predicting time series. In the traditional exponential smoothing method, a fixed weight is assigned to data history, and the trend changes of time series are ignored. In this paper, an uncertainty reasoning method, based on cloud model, is employed in time series prediction, which uses cloud logic controller to adjust the smoothing coefficient of the simple exponential smoothing method dynamically to fit the current trend of the time series. The validity of this solution was proved by experiments on various data sets.
基金Supported by the National Natural Science Foundation of China under Grant No 60972106the China Postdoctoral Science Foundation under Grant No 2014M561053+1 种基金the Humanity and Social Science Foundation of Ministry of Education of China under Grant No 15YJA630108the Hebei Province Natural Science Foundation under Grant No E2016202341
文摘The contribution of this work is twofold: (1) a multimodality prediction method of chaotic time series with the Gaussian process mixture (GPM) model is proposed, which employs a divide and conquer strategy. It automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. (2) An effective sparse hard-cut expec- tation maximization (SHC-EM) learning algorithm for the GPM model is proposed to improve the prediction performance. SHO-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning based on these pseudo inputs. Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction, but also the prediction confidence interval SHC-EM outperforms the traditional variational 1earning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning.
基金Project(61025015) supported by the National Natural Science Funds for Distinguished Young Scholars of ChinaProject(21106036) supported by the National Natural Science Foundation of China+2 种基金Project(200805331103) supported by Research Fund for the Doctoral Program of Higher Education of ChinaProject(NCET-08-0576) supported by Program for New Century Excellent Talents in Universities of ChinaProject(11B038) supported by Scientific Research Fund for the Excellent Youth Scholars of Hunan Provincial Education Department,China
文摘In order to effectively analyse the multivariate time series data of complex process,a generic reconstruction technology based on reduction theory of rough sets was proposed,Firstly,the phase space of multivariate time series was originally reconstructed by a classical reconstruction technology.Then,the original decision-table of rough set theory was set up according to the embedding dimensions and time-delays of the original reconstruction phase space,and the rough set reduction was used to delete the redundant dimensions and irrelevant variables and to reconstruct the generic phase space,Finally,the input vectors for the prediction of multivariate time series were extracted according to generic reconstruction results to identify the parameters of prediction model.Verification results show that the developed reconstruction method leads to better generalization ability for the prediction model and it is feasible and worthwhile for application.
基金Supported by the National Natural Science Foundation of China (69602002, 69772027)Doctoral Program Fund of State Education Commission(96056112)Natural Science Fund of Guangdong Province(960227, 963037)
文摘This paper presents some nonlinear models for time series. The structures and training methods for each model have been analyzed and studied. Experimental results for some common time series are given.
文摘The prediction methods for nonlinear dynamic systems which are decided by chaotic time series are mainly studied as well as structures of nonlinear self-related chaotic models and their dimensions. By combining neural networks and wavelet theories, the structures of wavelet transform neural networks were studied and also a wavelet neural networks learning method was given. Based on wavelet networks, a new method for parameter identification was suggested, which can be used selectively to extract different scales of frequency and time in time series in order to realize prediction of tendencies or details of original time series. Through pre-treatment and comparison of results before and after the treatment, several useful conclusions are reached: High accurate identification can be guaranteed by applying wavelet networks to identify parameters of self-related chaotic models and more valid prediction of the chaotic time series including noise can be achieved accordingly.
文摘Water level prediction of river runoff is an important part of hydrological forecasting.The change of water level not only has the trend and seasonal characteristics,but also contains the noise factors.And the water level prediction ability of a single model is limited.Since the traditional ARIMA(Autoregressive Integrated Moving Average)model is not accurate enough to predict nonlinear time series,and the WNN(Wavelet Neural Network)model requires a large training set,we proposed a new combined neural network prediction model which combines the WNN model with the ARIMA model on the basis of wavelet decomposition.The combined model fit the wavelet transform sequences whose frequency are high with the WNN,and the scale transform sequence which has low frequency is fitted by the ARIMA model,and then the prediction results of the above are reconstructed by wavelet transform.The daily average water level data of the Liuhe hydrological station in the Chu River Basin of Nanjing are used to forecast the average water level of one day ahead.The combined model is compared with other single models with MATLAB,and the experimental results show that the accuracy of the combined model is improved by 7%compared with the traditional wavelet network under the appropriate wavelet decomposition function and the combined model parameters.
文摘地心运动会影响地球参考框架原点的准确性,是地球参考框架进行非线性维持必须考虑的因素之一,因此提出对地心运动进行多尺度的建模和预测,以实现毫米级地球参考框架的建立和维持。采用网平移法计算的地心运动、全球地球物理流体中心(global geophysical fluids center,GGFC)和国际GNSS服务(international gnss service,IGS)第三次重处理(IGSR03)提供的3组地心运动数据,首先对其一致性和差异进行了分析,然后分别利用谐波模型和Diff-LSTM模型对地心运动进行了长期和短期的建模与预测,结果显示,GGFC地心运动的预测精度优于1.5 mm,而Diff-LSTM模型的地心运动预测结果在短期内优于谐波模型,当预测步长为17时,GGFC和IGSR03的地心运动预测精度均能达到甚至优于1 mm。表明地心运动的预测精度能够满足基于地球质量中心(center of mass of the total earth system,CM)的瞬时地球参考框架的建立与维持。