本文在传统时间序列分析建模方法的基础上，提出了用AR模型的新的完整而又简单的辨识方法。
On the basis of traditional time series analysis and modeling methods, the thesis puts forward a new complete and simply identification method by using ar model.
分析了传统时间序列分析法建立ARMA模型的不足，提出了一种利用模型阶数判断准则和长自回归法建模的新方法。
The disadvantage of establishing ARMA model with traditional time series analysis is analyzed; a new model building method based on judgment rules and long autoregression is put forward.
电力系统短期负荷预测使用的方法有传统建模方法，诸如时间序列、回归分析等方法。
There are traditional model methods of forecasting short-term load, such as time series, regression analysis, and so on.
预测实例表明，相比于传统的时间序列分析方法，这种预测方法能对振动响应的趋势进行更准确的预测。
A forecast instance indicates that compared with the traditional time series analysis, the present forecast method can carry out more accurate forecast of vibration response trends.
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.
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。
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.
采用非线性混沌时间序列预报，不同于传统的时间序列分析方法。
It differs from traditional time-sequence analysis methods in which nonlinear chaotic time-sequence prediction is used.
文中还论述了采用传统方法（FFT分析）与现代方法（时间序列分析）在微型机上实现信号处理时的有关问题。
Some problems concerning signal processing by micro-computer using the traditional method (the FFT analysis) and modern method (the time series analysis) are investigated. Experimental results ar…
文中还论述了采用传统方法（FFT分析）与现代方法（时间序列分析）在微型机上实现信号处理时的有关问题。
Some problems concerning signal processing by micro-computer using the traditional method (the FFT analysis) and modern method (the time series analysis) are investigated. Experimental results ar…
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