提出一种基于非线性自回归时间序列模型(gereral expression for linear and nonlinear auto-regressive mod-el,简称GNAR模型)的机械系统状态识别与故障诊断方法。利用采集系统工作过程中的特征信号建立GNAR模型;用主成分分析策略生成...提出一种基于非线性自回归时间序列模型(gereral expression for linear and nonlinear auto-regressive mod-el,简称GNAR模型)的机械系统状态识别与故障诊断方法。利用采集系统工作过程中的特征信号建立GNAR模型;用主成分分析策略生成模型特征量,对训练样本的特征量进行识别和分类,得到各种参考模式;将几何距离判别函数作为状态分类的原则,根据待判系统特征量与各类参考模式的Euclide距离进行状态识别和故障判别。对车床颤振试验数据及高速离心空气压缩机故障数据的分析表明,该方法快捷、高效,诊断成功率较好,具有良好的工程应用前景。展开更多
This paper mainly deals with the Bayesian statistical inference theory on the VAR(p) forecasting model based on the parameters’ Minnesota conjugate prior distribution,including the prior distribution’s structure, th...This paper mainly deals with the Bayesian statistical inference theory on the VAR(p) forecasting model based on the parameters’ Minnesota conjugate prior distribution,including the prior distribution’s structure, the parameters’ posterior distribution, and compares the forecasting accuracy of AR,VAR and BVAR model.展开更多
Time series is a kind of data widely used in various fields such as electricity forecasting,exchange rate forecasting,and solar power generation forecasting,and therefore time series prediction is of great significanc...Time series is a kind of data widely used in various fields such as electricity forecasting,exchange rate forecasting,and solar power generation forecasting,and therefore time series prediction is of great significance.Recently,the encoder-decoder model combined with long short-term memory(LSTM)is widely used for multivariate time series prediction.However,the encoder can only encode information into fixed-length vectors,hence the performance of the model decreases rapidly as the length of the input sequence or output sequence increases.To solve this problem,we propose a combination model named AR_CLSTM based on the encoder_decoder structure and linear autoregression.The model uses a time step-based attention mechanism to enable the decoder to adaptively select past hidden states and extract useful information,and then uses convolution structure to learn the internal relationship between different dimensions of multivariate time series.In addition,AR_CLSTM combines the traditional linear autoregressive method to learn the linear relationship of the time series,so as to further reduce the error of time series prediction in the encoder_decoder structure and improve the multivariate time series Predictive effect.Experiments show that the AR_CLSTM model performs well in different time series predictions,and its root mean square error,mean square error,and average absolute error all decrease significantly.展开更多
A nonparametric test for normality of linear autoregressive time series is proposed in this paper.The test is based on the best one-step forecast in mean square with time reverse.Some asymptotic theory is developed fo...A nonparametric test for normality of linear autoregressive time series is proposed in this paper.The test is based on the best one-step forecast in mean square with time reverse.Some asymptotic theory is developed for the test,and it is shown that the test is easy to use and has good powers.The empirical percentage points to conduct the test in practice are provided and three examples using real data are included.展开更多
文摘提出一种基于非线性自回归时间序列模型(gereral expression for linear and nonlinear auto-regressive mod-el,简称GNAR模型)的机械系统状态识别与故障诊断方法。利用采集系统工作过程中的特征信号建立GNAR模型;用主成分分析策略生成模型特征量,对训练样本的特征量进行识别和分类,得到各种参考模式;将几何距离判别函数作为状态分类的原则,根据待判系统特征量与各类参考模式的Euclide距离进行状态识别和故障判别。对车床颤振试验数据及高速离心空气压缩机故障数据的分析表明,该方法快捷、高效,诊断成功率较好,具有良好的工程应用前景。
文摘This paper mainly deals with the Bayesian statistical inference theory on the VAR(p) forecasting model based on the parameters’ Minnesota conjugate prior distribution,including the prior distribution’s structure, the parameters’ posterior distribution, and compares the forecasting accuracy of AR,VAR and BVAR model.
基金Shanxi Provincial Key Research and Development Program Project Fund(No.201703D111011)。
文摘Time series is a kind of data widely used in various fields such as electricity forecasting,exchange rate forecasting,and solar power generation forecasting,and therefore time series prediction is of great significance.Recently,the encoder-decoder model combined with long short-term memory(LSTM)is widely used for multivariate time series prediction.However,the encoder can only encode information into fixed-length vectors,hence the performance of the model decreases rapidly as the length of the input sequence or output sequence increases.To solve this problem,we propose a combination model named AR_CLSTM based on the encoder_decoder structure and linear autoregression.The model uses a time step-based attention mechanism to enable the decoder to adaptively select past hidden states and extract useful information,and then uses convolution structure to learn the internal relationship between different dimensions of multivariate time series.In addition,AR_CLSTM combines the traditional linear autoregressive method to learn the linear relationship of the time series,so as to further reduce the error of time series prediction in the encoder_decoder structure and improve the multivariate time series Predictive effect.Experiments show that the AR_CLSTM model performs well in different time series predictions,and its root mean square error,mean square error,and average absolute error all decrease significantly.
基金This research is supported by the National Natural Science Foundation of China(No.19971093) the Knowledge Innovation Program of the Chinese Academy of Sciences (No. KZCX2-SW-118).
文摘A nonparametric test for normality of linear autoregressive time series is proposed in this paper.The test is based on the best one-step forecast in mean square with time reverse.Some asymptotic theory is developed for the test,and it is shown that the test is easy to use and has good powers.The empirical percentage points to conduct the test in practice are provided and three examples using real data are included.
