提出了两种类型负荷预报模型，基于建筑模型辩识的负荷预报法和基于时间序列的负荷预报法。
Two kinds of models are derived; load prediction model based on building model recognition and load prediction model based on time series analysis.
针对供热过程的特点及节能控制的需要，提出基于RBF神经网络的时间序列交叉供热负荷预报法。
According to the characteristics of heat supply and the demands of energy-saving control, heat load forecasting based on RBF neural network and time series crossover is proposed.
文中首先介绍了时间序列法预报原理，接着应用该原理给出供热负荷和模型误差的预报。
First, it introduces time series analysis principle. Then, heating load and model error prediction are given by this principle.
时间序列分析法在水文规律分析、水文模拟以及水文预报等许多方面都起着重要作用。
Time series analysis method is playing an important role in hydrologic regular analysis and hydrologic analogy as well as hydrologic forecasting and so forth.
结果表明，浪高时间序列存在混沌现象，混沌时间序列法可应用于海浪预报的研究。
Results show that wave amplitude series have chaotic characteristic and chaos time series is feasible to be applied in wave forecast study.
时间序列分析法亦可用于船舶纵摇、艏摇的时间序列预报，该方法在工程中具有很大的实用价值。
The time series analysis can also be used in ship pitching and heaving time series prediction. These indicate that the prediction method is valuable for engineering practice.
介绍了自回归时间序列分析法的建模预报原理，并给出了船舶纵摇运动预报应用实例。
This article mainly introduces the principle of the prediction for auto regression modeling and recommends the AR arithmetic of prediction for ship pitching motion in detail.
径流中长期预报一直以来都是人们关注的热点研究问题，常用的时间序列法、多元回归分析法等都存在预报精度偏差过大的问题。
There are many methods for medium- and long-term runoff forecasting, such as time series, multiple linear regression and so on, which often have deviation in forecasting precision.
径流中长期预报一直以来都是人们关注的热点研究问题，常用的时间序列法、多元回归分析法等都存在预报精度偏差过大的问题。
There are many methods for medium- and long-term runoff forecasting, such as time series, multiple linear regression and so on, which often have deviation in forecasting precision.
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