In this paper, the theory and method of fuzzy time series analysis are presented, the model form and the parameters estimate problem are studied.
本文提出了模糊时间序列分析的理论和方法，研究了模型形式及其参数估计问题。
The paper is to organically combine the time series analysis method and neural network technology in the fuzzy control technology and fractal theory to predict mine gas emission quantity.
将模糊控制技术、分形理论中的时间序列分析方法与神经网络技术有机地结合起来，并运用于矿井瓦斯涌出量的预测中。
The nonlinear dynamic theory and the chaotic time series analysis method were adopted to examine the nonlinear characteristics of strong earthquake ground motions in this paper.
引入非线性动力学理论和混沌时间序列分析方法考察强震地面运动加速度时程的非线性特征。
Time series analysis based on neural networks theory cross through traditional frame of subjective model draw out prediction on the inner rules of linear time series data.
基于前向型神经网络理论的时间序列分析跳出了传统的建立主观模型的局限，通过时间序列的内在规律作出分析与预测。
To understand the frequency and chaotic characteristics of the particle colliding force, the time series of particle colliding force are analyzed by power spectrum analysis and chaotic theory.
通过对颗粒碰撞压力时间序列的功率谱分析和混沌分析，研究了其频域特性和混沌特征。
Finally, We designed a pseudo experiment to talk about the linear time series analysis based on neural networks theory.
最后，设计模拟实验，探讨有关神经网络的线性时间序列预测方面的问题，得出结论。
This thesis discusses period analysis methods of hydrological time series, and compares them from theory, measured sample calculation and Monte-Ca.
本文从理论基础、实测样本计算和统计试验方法三个方面对水文时间序列的周期分析检测方法进行了分析研究。
Based on random process theory and time series analysis, the paper advanced the adaptive combined smoothing model suiting to seasonality, trend and randomness of water consumption series.
利用随机过程及时间序列分析手段，根据用水量序列季节性、趋势性及随机扰动性的特点，建立了用水量预测的自适应组合平滑模型。
In this paper, the Theory of Corrclation Analysis on Time Series is applied to very-short-term power system load forecast.
本文将时间序列相关分析理论应用于电力系统超短期负荷预报。
Traditional linear cointegration theory and ECM can be used widely in time series analysis.
传统的线性协整和ECM在现代时间序列分析中得到广泛应用。
Traditional linear cointegration theory and ECM can be used widely in time series analysis.
传统的线性协整和ECM在现代时间序列分析中得到广泛应用。
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