Time series data set comes with a temporal ordering.
时间序列数据集伴随着一个时间上的排序。
Returns predicted future or historical values for time series data.
返回时序数据的将来或历史的预测值。
How to detect if change in time series data is no longer significant?
如何检测是否在时间序列数据的变化不再明显？
How to customize axis when plot multiple time series data in 1 panel?
如何自定义轴当绘制多个时间序列数据在1小组？
A time series data set is a sequence of random variables indexed by time.
时间序列数据是以时间为指标的一个随机变量序列。
In the field of customer, there exist a large number of time series data.
在客户领域，存在着大量的时间序列数据。
How to remove subjects who have missing measurements in time series data?
如何去除那些失踪的测量在时间序列数据吗？
Research on time series data mining is one of important hot spots of data mining.
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。
Time series data is continuous and can be stored in a nested table or in a case table.
时序数据是连续的，可以存储在嵌套表或事例表中。
RRDtool is an open source, high performance data logging and graphing system for time series data.
RRDtool是一个开放源码的高性能数据日志记录和绘图系统，用于处理时间系列数据。
In the last, we give an example of dynamical Bayesian networks for time series data analysis.
最后给出了用于时间序列分析的动态贝叶斯网络的实例。
In the near future at clustering periodic time series data study up have more and more extensive trend.
近期在周期性时间序列资料分群研究上有越来越广泛的趋势。
Based on the project background, an improved outlier data mining algorithm for time series data is given out.
根据课题背景，给出一个针对时序数据的离群数据挖掘算法的改进算法。
Mapping the raw time series data to a modality space effectively is a critical problem in time series similarity search.
将时序数据有效地映射到特征空间是时间序列相似性搜索的一个关键问题。
In this paper, the method TSD-PVAP, which is the pixel-oriented visualization analysis of time series data is introduced.
提出了一种基于像素的时序数据可视化分析方法TSD -PVAP。
Rough set theory, as an effective tool to deal with vagueness and uncertainty, is effective to the time series data mining.
粗糙集理论作为一种处理模糊和不确定性问题的有效工具，对时间序列的数据挖掘是有效的。
The process of knowledge discovery in time series includes preprocessing of time series data, attributes reduction and rules extraction.
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。
In this paper, we extract rules of the decision table by an incremental algorithm for the dynamic decision table of the time series data.
本文对数据成时间序列的动态决策表，用增量式算法提取决策表的规则模型。
The results show that the proposed ODP is an effective and feasible technique to extract the features from the hyperdimensional time series data.
同时，以心电信号为例对ODP方法进行测试，结果表明，该方法应用于超高维数据的特征提取是行之有效的。
This hybrid model synthesizes the merits of multiple intelligent computation methods and offers a new effective solution of time series data mining.
该混合模型融合多种智能计算方法优点于一体，为时序数据挖掘提供了一种新的实用方法。
Another speaker discussed the challenges of managing time series data, meaning that you track incoming data according to the time interval when it was recorded.
另一位演讲者探讨了管理时间系列数据的挑战，表示您依据记录所传入数据的时间间隔来跟踪该数据。
To overcome the shortage of historical data, the increment of learning samples are got by clustering analysis the time series data from Ticket sale record.
为了克服历史数据不足的问题，设计了通过时间序列聚类分析进行学习样本集的积累的方法。
Incidence degree could be used to analyse time series data in health system. There is little limit about data distribution and type of variable's correlation.
关联度分析方法可用于卫生系统内部时间序列资料的分析，该方法对数据分布类型、变量之间的相关类型限制较少。
The model for filling time series data of traffic flow based on LS-SVM is proposed in this paper, missing data can be filled by using traffic flow historical data.
利用实例仿真验证表明，LS-SVM具有较好的泛化能力和很强的鲁棒性，采用基于LS-SVM的交通流时间序列模型补齐丢失数据能够取得很好的效果。
While applying the method to the time series data of sedimentary rock in Talimu basin, the prediction and classification of layer in petroleum well have got solution.
在塔里木盆地沉积岩时间序列化学数据的应用实例中，解决了石油井下地层预测和归类问题。
A way of time series data mining was put forward based on the exploratory analysis and the mathematics module was founded by way of using linear regression technology.
提出了基于探索性分析的时序数据挖掘方法，采用线性回归技术建立了数学模型。
Firstly, making the time series continuous through inserting data, and secondly removing the secular displacement rate from the time series data through linear fitness.
首先对时间序列中不连续的数据进行内插处理，并通过线性拟合从时间序列中去掉长期滑动速率的影响。
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.
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。
Lastly, the feasibility and validity of the model was validated with the past years surface water resource quantity time series data from Kenswat Station on Xinjiang Manas River.
最后，以玛纳斯河肯斯瓦特站历年的年径流资料验证时间序列人工神经网络预测模型的可行性与有效性。
Lastly, the feasibility and validity of the model was validated with the past years surface water resource quantity time series data from Kenswat Station on Xinjiang Manas River.
最后，以玛纳斯河肯斯瓦特站历年的年径流资料验证时间序列人工神经网络预测模型的可行性与有效性。
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