| 1. | Prediction modelling method for non - linear and non - stationary time series
一种非线性非平稳时间序列预测建模方法 |
| 2. | Application of hydrologic forecast in nen river and building of stable time rank model
平稳时间序列模型建立及在水文预报中的应用 |
| 3. | The model of non - stable time series raised by box - jenkins is applied to give a mathematical prediction of the deformation tendency
采用box - jenkins非平稳时间序列模型，对码头的变形趋势进行了中、短期时间预测。 |
| 4. | Emd method is local adaptive . lt not only can be used to analyse linear data , but also can be used to analyse nonlinear and non - stationary data
Emd方法是一种局部自适应方法，不仅适用于线性时间序列的分析，而且适用于非线性和非平稳时间序列的分析。 |
| 5. | After fractional order differencing , the long momory series of dowjones index and nasdaq index become stationary , but the fractional order of two indexes is different , so there does not exist linear cointegration relation between them
摘要首先对道琼斯工业指数进行分整分析，长记忆的道指被分数维差分得到平稳时间序列；进而对纳指也进行分数维差分。 |
| 6. | Cointegration method is a new algorithm developed in economics area for modelling non - stationary economical and financial processes . it can generate a stationary process based on a linear combination of a set of non - stationary processes if these non - stationary processes have long - term balanced relation
协整理论是处理非平稳时间序列的有力工具,可通过线性组合,将具有长期均衡关系的多个非平稳时间序列生成一个平稳序列。 |
| 7. | Firstly , utilizing grey - separate model makes time - series take speadily , that is to say , use the grey model to prune the trend one x ( t ) ; and the array got is a steady time array y ( t ) ; secondly , using arima models y ( t ) ; lastly , we get combined predication model of w ( t )
首先利用灰色分离模型法使时间序列平稳化，即利用灰色模型削去趋势项x （ t厂得到的序列既是平稳时间序列y （ t广然后利用aaima模型法对y （ t ）建模，最后得到原始数列叫t ）的组合预测模型。 |
| 8. | ( 6 ) it studies the time serial neural network forecast model and discusses how to determine the dimension of input data and how to smooth the unsteady time serial data . also this paper uses the time serial neural network model to forecast the volume of freight of shenzhen airport air logistics park . finally , this paper attains higher accuracy in using the forecasting model and gets a good forecast effect
( 6 )对基于时间序列的神经网络预测模型进行了研究，并研究了输入维数的确定及非平稳时间序列如何平稳化的问题，并利用时间序列神经网络预武汉理工大学博士学位论文测模型进行深圳航空物流园区的货流量的预测，从而提高了预测模型的精度。 |
| 9. | As far as the nonstationarity during the long period operation of machinery was concerned , the application of adaptive linear element ( adaline ) neural network to prediction of nonstationary time series was studied . the relationship between adaline and auto regressive ( ar ) model was analyzed , and the method to determine the number of input neurons in adaline prediction model according to bic criteria was presented . the effect of the adaptive learning rate on prediction was also analyzed
针对生产实践中设备运行的非平稳性，基于动态预测思想，研究了非平稳时间序列的自适应线性单元( adaline )神经网络预测，讨论了adaline和自回归( ar )模型之间的关系，提出根据ar模型定阶方法确定adaline预测模型的输入神经元数目，分析了自适应学习率对预测性能的影响，为机械设备状态预测提供了一种方法。 |
