Thanks to frameworks such as Spark’s GraphX and GraphFrames, graph-based techniques are increasingly applicable to anomaly, outlier, and event detection in time series. However, most data do not naturally come in the form of a network that can be represented in graphs. Therefore, it is not clear whether graph-based techniques always offer the most appropriate approach to detect anomalies.
Jeffrey Yau offers an overview of applying graph-based techniques and outlines the benefits of graphs relative to other techniques. Jeffrey compares and contrasts the use of graph theory and techniques, large-scale time series mining methods, and traditional parametric linear and nonlinear time series techniques in anomaly, outlier, and event detection—with specific examples from credit card fraud, wearable IoT devices, and financial time series.
An expert in quantitative modeling with a strong background in finance, Jeffrey Yau has over 17 years of experience applying econometric, statistic, and mathematical modeling techniques to real-world challenges. As vice president of data science at Silicon Valley Data Science, Jeffrey has a passion for leading data science teams in finding innovative solutions to challenging business problems.
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