门限自回归模型

  • 网络Threshold Autoregressive Model;TAR
门限自回归模型门限自回归模型
  1. 门限自回归模型参数估计的渐近性质

    Asymptotic properties of estimates of parameters for tar models

  2. 结果表明:门限自回归模型计算较为简便且便于计算机自动建模。

    TAR model is convenient to calculate and can be built by computer automatically .

  3. 门限自回归模型(TAR)是一种分段线性的非线性时间序列模型。

    Threshold autoregressive model ( TAR ) is a nonlinear sequential model which is segmentedly linear .

  4. 实例分析表明,所建模型预测误差均较小,好于门限自回归模型,BP神经网络模型和ELMAN神经网络模型。

    The prediction precision of this RBF model is higher than that of the threshold auto-regression model , the BP artificial neural network model and the ELMAN artificial neural network model .

  5. 非负门限自回归模型NTAR(1)

    Nonnegative - threshold autoregressive models ntar ( 1 )

  6. 根据年径流时间序列资料所隐含的时序分段相依性,用门限自回归模型(TAR)来预测年径流,并研制了TAR建模的一整套简便通用的方案。

    To effectively utilize information of the section interdependence in the time series of annual runoff , a threshold auto-regressive ( TAR ) model is proposed to predict annual runoff . A simple and general scheme is presented to establish a TAR model .

  7. 结合感潮河段非平稳时间序列的动态特性,引入ARIMA模型、基于遗传算法的门限自回归模型对感潮河段的水文序列成功的进行了预报尝试。

    Combined with analyzing the dynamic characteristics of non-stable hydrologic time sequences of tidal rivers , The ARIMA model and TAR model based on genetic algorithm are both excerpted into the prediction field of the tidal rivers successfully .

  8. 门限自回归模型中门限和延时的小波识别

    Wavelet Identification of Thresholds and Time Delay in Threshold Autoregressive Models

  9. 门限自回归模型被广泛地用于许多领域。

    Threshold autoregressive models are widely used in time series applications .

  10. 门限自回归模型参数最小二乘估计的强收敛速度

    Strong convergence rates of parametric estimation for threshold autoregressive model

  11. 门限自回归模型参数的局部正交设计寻优法

    An optimizing method of threshold auto-regressive model parameters in partial orthogonal design

  12. 枯水径流预报的最优模糊划分自激励门限自回归模型

    Optimal Fuzzy Partitioned Self excited Threshold Autoregressive Model for Low Flow Forecast

  13. 门限自回归模型与非线性系统的极限环

    Threshold Autoregressive Model and Limit Cycle of a Nonlinear System

  14. 多维门限自回归模型参数估计的渐近正态性

    Asymptotic Normality of Coefficient Estimate for Multidemensional Thereshold Autoregression Model

  15. 遗传门限自回归模型的改进及其应用

    Improvement of Threshold Auto-regressive Model Based on Genetic Algorithm and Its Application

  16. 用于机械故障诊断的门限自回归模型盲辨识

    Blind identification of threshold auto-regressive model for machine fault diagnosis

  17. 门限自回归模型在预测岩溶泉水流量中的应用&桂林岩溶试验场

    Threshold autoregressive model applied to prediction of karst spring flow

  18. 自激励门限自回归模型在枯水径流预报中的应用

    Application of Self-Excited Threshold Autoregressive Model to Low Flow Forecast

  19. 门限自回归模型和非参数模型中变点的小波分析

    Wavelet Analysis for Change Points in Threshold Autoregressive Models and Nonparametric Models

  20. 门限自回归模型建模的有关探讨及其在人口死亡率预报中的应用

    Discussion on threshold auto - regressive model and its application to mortality prediction

  21. 数值实例表明,逐步门限自回归模型在模拟和预报稳定上比一般门限自回归模型有一定程度的提高。

    Numerical experiments show that the designed model is better to some extent .

  22. 年径流预测的遗传模拟退火门限自回归模型

    Annual Runoff Prediction Based on GA and Simulated Annealing

  23. 具有时变参数的门限自回归模型及其在气候预报中的应用

    A time dependent parametric threshold autoregressive model and its application on climate forecast

  24. 遗传门限自回归模型在感潮河段水位预测中的应用

    Application of genetic threshold auto-regressive model to water stage forecasting for tidal river section

  25. 本文根据门限自回归模型的基本思想[1],提出一种多元门限回归模型的建模方法。

    In this paper , a method of building multivariate threshold regression model is given .

  26. 门限自回归模型建模方法的改进

    An improved modeling method for threshold Autoregression

  27. 逐步门限自回归模型及其建模方案

    Designing of the stepwise threshold autoregressive model

  28. 开环门限自回归模型

    The autoregressive model for open-loop threshold

  29. 岩溶裂隙水动态系统的门限自回归模型分析

    The threshold autoregressive model analysis of karst-fissure water dynamic system in northern karst area of China

  30. 基于遗传算法的门限自回归模型在浅层地下水位预测中的应用

    Threshold auto regressive model based on genetic algorithm and its application to forecasting the shallow groundwater level