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。
If you create two profile items with the same data item UID, and which overlap the same time, then the AMEE API will complain as it can't form a sensible time series from the data.
如果您使用相同的数据项uid创建了两个配置项，而且时间重叠，那么AMEEAPI将会报错，因为它不能从该数据形成一个合理的时间时序。
AMEE allows you to store time information along with your data, so that you can build up a series of data points over time.
AMEE允许在存储数据的同时存储时间信息，以便根据时间构建一系列数据点。
URI versioning […] is a design choice when resources are immutable across time and we create new resources for state changes (similar to how we manage time-series data in a database).
URI版本控制 […]是一种设计决定，用于当资源不随时间的变迁而变化时，我们为状态的改变创建新资源（类似于管理数据库中的时间序列数据）。
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。
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。
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。
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.
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。
Furthermore, the key problem of decision making in time series information system is how to effectively mine the time order information in history data.
时间序列信息系统中的决策问题的关键是有效地挖掘历史数据中包含的时序信息。
Based on measured data of micro silicon gyro and time-series theory, the AR model of gyro static drift is established, then the continuous-time differential equation is got.
利用微硅陀螺测量的数据，运用过程辨识理论和时间序列分析方法，建立了陀螺静态漂移的自回归（AR）模型，进而得到连续微分方程。
Focusing on the problem of data mining in time-series, did research in transforming time-series to trend sequences and methods of performing data mining in acquired trend sequences.
本文针对时间序列的数据挖掘问题，研究了将时间序列转化为趋势序列，以及趋势序列中的数据挖掘问题。
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。
Simultaneously, the forecast theory and method of nonlinear time series is established, which combines mechanism of the time space system with analyzing historical data.
结合时空系统机制和历史资料的分析，建立非线性时空序列预测理论与方法。
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。
Among these research fields, time series data mining is a rather complex branch, which is a technique that extracts the most valuable information from large amount of history time series data.
而在这其中时间序列数据挖掘是面向特殊应用数据挖掘领域中比较复杂的一个分支，主要研究从大量时间序列历史数据中挖掘有价值信息的方法和相关技术。
Financial time series has high randomicity and nonlinearity. Neural network is quite suitable in the process of financial time series data for its good ability of nonlinear mapping and generalization.
金融时间序列具有很强的随机性和非线性性，而神经网络具有良好的非线性映射能力及自适应、自学习和良好的泛化能力，因此非常适合处理金融时间序列这样的数据。
Time series data is the data set that arranges every one according to the time, and it USES social, economic and technologic fields widely.
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 is very important and complicated data object. There are a lot of time series need to be further analyzed and processed in the all kinds of areas in society.
时间序列数据是一种复杂的数据对象，在社会生活中的各个领域广泛存在着大量的时间序列数据有待于进一步的分析和处理。
In order to improve the efficiency of filtering algorithms for time series data stream, this paper proposes a new more efficient streaming time series query filtering algorithm for DTW.
目的设计基于DTW的高效过滤算法，提高时间序列数据流的过滤查询的效率。
However, there are a few on multivariate time series mining, since the data structure of multivariate time series is more complex than that of univariate time series.
然而多元时间序列的数据结构比一元时间序列更复杂，现有的理论和方法仍不够完善。
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.
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。
In our daily life, there are various kinds of time series data, and time series prediction becomes one of the important aspects of data Mining and Knowledge Discovery (DMKD).
在日常生活中广泛存在着各种时间序列数据，发现时间序列知识、对时间序列进行预测正成为数据挖掘与知识发现的重要内容。
The pattern representation of time series itself has the function of compress data and keeps the basic shape of time series, and it has a certain extent effect of deleting noises.
时间序列的模式表示本身就具有压缩数据、保持时间序列基本形态的功能，并且具有一定的除噪能力。
At the same time, however, the nonlinear and chaotic characteristic of time-series data makes the mining be a difficult issue.
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。
Longitudinal data is referred to data in which individuals are measured repeatedly through time, so it combines elements of cross-sectional data and time-series data.
纵向数据是指对每个个体在不同时间进行观测而得到的由截面和时间序列融合在一起的数据。
Longitudinal data is referred to data in which individuals are measured repeatedly through time, so it combines elements of cross-sectional data and time-series data.
纵向数据是指对每个个体在不同时间进行观测而得到的由截面和时间序列融合在一起的数据。
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