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Related Concept Videos

Time-Series Graph00:54

Time-Series Graph

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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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Multiple Bar Graph01:07

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
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Graphical and Analytic Representation of Sinusoids01:20

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Analyzing two sinusoidal voltages with equal amplitude and period but different phases on an oscilloscope, an instrument used to display and analyze waveforms, involves a three-step process.
The first step is measuring the peak-to-peak value, which is twice the amplitude of the sinusoid. This provides information about the maximum voltage swing of the waveform.
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Bar Graph01:07

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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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Review and Preview01:13

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Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
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End Point Prediction: Gran Plot01:07

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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.
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    Graph neural networks (GNNs) offer advanced time series analysis by modeling complex relationships. This survey reviews GNNs for time series forecasting, classification, anomaly detection, and imputation, guiding future research and applications.

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    Area of Science:

    • Data Science
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Time series data are crucial for understanding dynamic systems, generated by physical and virtual sensors.
    • Traditional methods struggle to capture complex inter-temporal and inter-variable relationships in time series data.
    • Recent advancements in Graph Neural Networks (GNNs) show promise for enhanced time series analytics.

    Purpose of the Study:

    • To provide a comprehensive review of GNNs applied to time series analysis (GNN4TS).
    • To guide researchers and practitioners in understanding, applying, and advancing GNN4TS.
    • To consolidate existing knowledge and highlight future research directions in GNN4TS.

    Main Methods:

    • A task-oriented taxonomy of GNN4TS is presented, covering forecasting, classification, anomaly detection, and imputation.
    • Representative research works and mainstream applications of GNN4TS are discussed.
    • The survey synthesizes foundations, practical applications, and future opportunities.

    Main Results:

    • GNNs excel at modeling explicit inter-temporal and inter-variable relationships in time series data.
    • The review covers key applications including forecasting, classification, anomaly detection, and imputation.
    • A structured overview of GNN4TS research is provided, identifying current trends and gaps.

    Conclusions:

    • GNNs represent a significant advancement in time series analysis capabilities.
    • This survey serves as a foundational resource for the GNN4TS field.
    • Future research should explore novel GNN architectures and applications for complex time series problems.