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。
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。
You can use the unique function to evaluate all the unique members found in a series data object.
可以使用UNIQUE函数处理在序列数据对象中找到的所有独特成员。
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。
If authors reported a subset of a multicenter study, the largest multicenter series data available was used.
如果作者报道的一个多中心研究中的一个亚组研究，那么至采用其中最大的多中心系列的数据。
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 hydrological method is using the hydrological series data to establish the autoregressive and multivariate recurrence models.
水文方法是利用水文序列资料建立自回归模型和多元递推模型。
Graphite is a Python web application that has been developed to provide scalable storage and visualization for numeric time-series data.
Graphite是一个PythonWeb应用，用来为数字时序数据提供可伸缩的存储和可视化显示。
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。
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.
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。
Combining grey system theory with the feature of series data, a grey interpolation approach based on forward and back grey prediction model is proposed.
根据灰色系统理论和序列数据的特性，提出一种灰插值方法。
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。
In Chapter four, Empirical tests using the time-series data in EastAsia and US money markets from 1993-2003 reveal that convergence tendency is found in the money market.
前一部分是第四章与第五章，深入研究1993-2003年间东亚经济体货币市场及国内利率体系与世界市场一体化的程度。
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。
It has important practical significance to analyze and process with the large number of time-series data and mine with the value of the underlying implication of information.
对于这些大量的时序数据进行分析处理，挖掘其背后蕴涵的价值信息，具有重要的实际意义。
Finally, the results show the methods can effectively come into being regression analysis model of time-series data streams, and fulfill the prediction of future data streams.
最后，试验分析展示了研究结果能够有效地产生时间序列数据流的回归模型和实现数据流未来数据的预测。
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