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相关概念视频

Time-Series Graph00:54

Time-Series Graph

4.2K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

169
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
169
Outliers and Influential Points01:08

Outliers and Influential Points

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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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What Are Outliers?01:12

What Are Outliers?

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Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
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Plotting of Topographic Maps01:29

Plotting of Topographic Maps

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Topographic maps represent the Earth's surface features using contour lines, which connect points of equal elevation to create a two-dimensional representation of three-dimensional terrain. Creating a topographic map requires a systematic approach.Begin by plotting a scaled grid and marking intersections corresponding to the survey's elevation data points. Assign elevation values at these intersections to build the base map. Next, determine contour levels using a consistent contour interval,...
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Scatter Plot01:15

Scatter Plot

6.6K
The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
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Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
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时间序列中的图形异常检测:一项调查.

Thi Kieu Khanh Ho, Ali Karami, Narges Armanfard

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    此摘要是机器生成的。

    本调查回顾了基于图形的时间序列数据异常检测,突出显示了图形表示.

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    科学领域:

    • 数据科学数据科学数据科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 时间序列数据在各个领域越来越普遍.
    • 时间序列异常检测 (TSAD) 对网络安全和医疗保健等应用至关重要.
    • 传统的TSAD方法与复杂的变量内和变量间的依赖性作斗争.

    研究的目的:

    • 为时间序列异常检测 (G-TSAD) 提供基于图形的方法的全面审查.
    • 探索图形表示在增强TSAD方面的潜力.
    • 确定G-TSAD当前的挑战和未来的研究方向.

    主要方法:

    • 关于最先进的G-TSAD技术的文献综述.
    • 对基于图形的TSAD应用的深度学习架构的分析.
    • 讨论审查的方法的优点,局限性和应用.

    主要成果:

    • 图形表示有效地捕捉时间序列数据中的复杂依赖关系.
    • 基于深度学习的图形方法对TSAD显著有前途.
    • 在G-TSAD领域确定了关键的技术和应用挑战.

    结论:

    • 在时间序列中,G-TSAD为先进的异常检测提供了一个强大的范式.
    • 需要进一步的研究来应对现有的挑战,并释放实际应用.
    • 该调查为基于图表的时间序列异常检测的未来进展提供了路线图.