The prediction was made in the volume of ferry in main routes by Time Sequence Prediction method.
运用时间序列预测法对环渤海地区主要航线的客（车）运量进行了预测；
Based on specific features of the neural network, this paper is concerned with its application to prediction of nonlinear time sequence.
针对神经网络的特点，探讨了神经网络对非线性时间序列预测的应用。
The traditional logistics transportation amounts prediction ways mainly indicate liner regression and time sequence model.
传统的物流运输量预测技术大多采用线性回归和时间序列技术。
The self-tuning prediction of the future value can be achieved on the basis of real time model-building and modification to the one-step predicting errors sequence in the exponential smooth predictor.
对指数平滑预报器一步预报误差序列进行实时建模和实时修正的基础上，实现对未来值的自校正预报。
Theorem of projection is more important in the study of prediction of stationary discrete-time sequence and function approximating.
投影定理在平稳时间序列的预报及函数逼近研究中起着重要的作用。
The pure prediction problem of a stationary random sequence passing through a linear time-invariant system is discussed.
本文讨论了平稳随机序列通过线性时不变系统后的纯预测问题。
In this paper, the time - sequence model of traffic flow is based on the improved BP neural network, and this model can be used for short time prediction of traffic flow.
本文采用改进型BP神经网络建立起交通流的时间序列模型，该模型可用于短期内道路交通流量的预测。
By analyzing short time traffic flow time sequence property, the gray system theory is used for short time traffic flow prediction and the scrolling GM (1, 1) prediction model is set up.
通过分析短时交通流时序特性，将灰色系统理论应用于短时交通流预测，建立了滚动GM(1,1)预测模型。
It differs from traditional time-sequence analysis methods in which nonlinear chaotic time-sequence prediction is used.
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。
Experimental results show that the proposed Volterra adaptive prediction model is capable of effectively predicting traffic flow time sequence and low-dimensional chaotic time sequence.
结果表明，该模型能够较准确地预测交通流量时间序列和低维混沌时间序列。
At the same time, we completed the comprehensive prediction of distribution of reservoir using the combination of analysis high resolution sequence stratigraphy and many geophysical technologies.
与此同时高分辨率层序地层分析与地球物理地层反演技术、属性提取技术、储层特征重构反演等技术相结合，完成储层分布的综合猜测。
At the same time, we completed the comprehensive prediction of distribution of reservoir using the combination of analysis high resolution sequence stratigraphy and many geophysical technologies.
与此同时高分辨率层序地层分析与地球物理地层反演技术、属性提取技术、储层特征重构反演等技术相结合，完成储层分布的综合猜测。
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