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.
时间序列数据集伴随着一个时间上的排序。
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?
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?
如何在机器学习的其他属性的处理时间序列数据？
How to handle time series data with other attributes in machine learning?
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.
这是因为面板数据包括时间序列数据和横截面数据。
This is because panel data consist of times series data and cross sectional data.
时间序列数据是以时间为指标的一个随机变量序列。
因此，企业可能会被迫在公司层面上使用时间序列数据。
As a result, the organization may be forced to conduct its evaluation at the corporate level using time series data.
然而，时间序列数据的非线性混沌特点，使得对它的挖掘成为难题。
At the same time, however, the nonlinear and chaotic characteristic of time-series data makes the mining be a difficult issue.
本文主要研究了时间序列数据挖掘方法中的序列模式和相似性搜索。
This paper analyses all kinds of algorithms used on sequential pattern mining and discusses traditional similarity search techniques.
知识发现的过程包括时间序列数据预处理、属性约简和规则抽取三部分。
The process of knowledge discovery in time series includes preprocessing of time series data, attributes reduction and rules extraction.
目前对于时间序列数据挖掘的研究主要集中在相似性搜索和模式挖掘上。
Opposite to mature part of data mining (such as mining of database association rules and classify rules), mining of time series still falls into a new branch.
但是，该方法不能反映具有时间序列数据的变化特征与趋势，无法提供正确的决策支持。
But the method can't timely and accurately reflect the change characters and the trends of the time series, and can't supply the right decision-making.
时间序列数据在数据库数据中十分普遍，于是对时间序列进行数据挖掘已成为当前研究的焦点之一。
As a very common type of the data sets, time series has been one of the focuses of the current data mining research.
由于这些变量具有非线性时间序列数据，用人工神经网络(ANN)将使用反向传播算法作为学习算法。
Since these variables are characterized as nonlinearities time series data, Artificial Neural networks (ANN) will be employed using back propagation algorithm as learning algorithm.
时间序列分析所论及的就是对这种依赖性进行分析的技巧，这要求对时间序列数据建立随机动态模型。
What time series mention is the analytical technique to this kind of dependence. This requires found stochastic and dynamic models of time series.
时间序列数据就是按时间先后顺序排列各个观测记录的数据集，广泛存在于社会、经济、技术等领域中。
Time series data is the data set that arranges every one according to the time, and it USES social, economic and technologic fields widely.
你甚至不必对这些时间序列数据进行分析，因为这个工具允许你做一个散点图来比较不同数据里的搜索趋势。
You don't even have to produce an analysis on time-series data, as the tool lets you do a scatter plot to compare search trends between different data.
时间序列存在于社会的各个领域，对于时间序列数据挖掘的研究目前主要集中在相似性搜索和模式挖掘上。
Recently the study on data mining of time series mainly concentrates on both the similarity search in a time series database and the pattern mining from a time series.
纵向数据的最大优点就是它将截面数据和时间序列数据结合在一起，更好地分析出个体随时间的变化趋势。
The prominent advantage of longitudinal data is that it can analyze effectively the change of individuals over time.
然而石油期货价格具有时间序列数据的典型特点，即非线性和非平稳性，这给价格的预测带来了极大的困难。
However, the oil futures prices involve the typical characteristics of time series data, nonlinearity and nonstationarity, which brings insuperable difficulties in the price forecasts.
本文利用1978 ~ 2004年的时间序列数据，运用计量方法分析我国农业贷款对农业产出增长的影响。
Basing on the time sequence data (1978-2004), this essay analyses the influence of agricultural loan on the development of agricultural economy by using metric method.
运用出口贸易增长的动因模型，选取时间序列数据，尝试对影响广东出口贸易增长的动因进行实证分析与研究。
With the factor model of export trade growth, by sampling chronical data, the paper probes into the factors promoting Guangdong export trade growth.
本课程意在达到两个目标：它提供了运算时间序列数据的工具并且对于时间序列模型的理论也会做基础的介绍。
The course intends to meet two goals. It provides tools for empirical work with time series data and is an introduction into the theoretical foundation of time series models.
赫斯特指数的计算过程需要对研究的时间序列数据进行分组，而不同的分组情况对赫斯特指数会产生不同的影响。
The calculation process of Hurst index needs to group the data of time sequence. And different grouping situations have different influences on the Hurst index.
对于一个实际的时间序列数据，我们并不知道其真正的数据生成过程，只能通过假设和基于假设的统计推断来确定。
We have to draw statistical inference by hypothesis testing, because we don't know the real data generation process of any time series.
另外，本文用模糊集理论对时间序列数据挖掘过程中的不确定性进行了处理，提出了一种模糊时序数据挖掘的框架。
Moreover, fuzzy sets theory is adopted in the dissertation to deal with the uncertainty of the mining process and a new fuzzy frame of TSDM is given then.
另外，本文用模糊集理论对时间序列数据挖掘过程中的不确定性进行了处理，提出了一种模糊时序数据挖掘的框架。
Moreover, fuzzy sets theory is adopted in the dissertation to deal with the uncertainty of the mining process and a new fuzzy frame of TSDM is given then.
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