模型作为时间序列运行。
以重要度排序列好清单以防时间不够。
List your questions from most important to least important, in case time runs out.
如何检测是否在时间序列数据的变化不再明显？
How to detect if change in time series data is no longer significant?
目前时间序列的数据挖掘是数据挖掘的重要研究热点之一。
Research on time series data mining is one of important hot spots of data mining.
在客户领域，存在着大量的时间序列数据。
In the field of customer, there exist a large number of time series data.
时间序列数据是以时间为指标的一个随机变量序列。
A time series data set is a sequence of random variables indexed by time.
时间序列的预测在经济和工程领域具有十分重要的意义。
The prediction of time series is very important in the economic and engineering fields.
本文讨论如何应用时间序列建模预测电力负荷。
This paper discusses the prediction of power load by time series modelling.
灰色系统预测法对时间序列的预测有较高的精度。
The grey system has a high precision for the time series prediction.
如何去除那些失踪的测量在时间序列数据吗？
How to remove subjects who have missing measurements in time series data?
如何自定义轴当绘制多个时间序列数据在1小组？
How to customize axis when plot multiple time series data in 1 panel?
这种方法可用于一个不平等的间隔时间序列。
This method can be used with an unequally spaced time series.
资金流转是时间序列的函数。
将非平稳时间序列的状态空间建模方法用于陀螺过渡过程的分析。
Stationary time series state space modeling method for the analysis of the transition process gyro.
门限自回归模型是一种新近创立的非线性时间序列摸型。
The threshold autoregressive model is a kind of non-linear time series model recently established.
趋势是经济时间序列最主要的特征之一。
Trend is one of the most important characters of economic time series.
本文提出一种混沌时间序列预测技术。
This paper presents a method of forecasting chaotic time series.
主要介绍了现代的信号分析法——时间序列分析法。
Mainly introduced the signal of the modern analysis method - time sequence analysis method.
时间序列模型主要是自回归模型。
然后，我们可通过股票检测来发现股票时间序列的异常点。
Then we can use the Score test to find the outliers in stock time series.
水文时间序列相似性查找模型。
它可以被用来研究时间序列的连贯性。
It can be applied to time series to investigate their persistency.
对经过预处理的测试数据进行三次样条插值，得到基本时间序列。
First, the testing data must be interpolated and the basic time series must be built.
机是一种新的计算力学理论，它能从时间序列中发掘系统的隐含模式。
Epsilon machine, a new computational mechanics, can discover hidden pattern from the response time series.
非平稳时间序列的状态空间建模技术被用于陀螺漂移分析。
A state space approach for the modeling of nonstationary time series is presented.
其作用呈时间依赖性及序列特异性。
The effect was in time dependent and sequence specific manner.
这样对季节性时间序列进行单位根检验是很必要的。
Thus, testing unit root in seasonal time series is necessary.
应用随机穿越理论分析了有限个随机滞留时间序列中的毁伤概率问题。
The killing probability in finite random stationary time series is studied by stochastic passage theory.
应用随机穿越理论分析了有限个随机滞留时间序列中的毁伤概率问题。
The killing probability in finite random stationary time series is studied by stochastic passage theory.
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