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18.07.11_useR2018 Poster_Time Series Digger : Automatic time series analysis for data science in R

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Time Series Digger : Automatic time series analysis for data science in R

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18.07.11_useR2018 Poster_Time Series Digger : Automatic time series analysis for data science in R

  1. 1. J They result in suitable detection Our application and data - Network traffic - Managing service and data center - Customer behavior All data is time series in the real-world We need effective and comprehensive data process for various problem settings ggAutoTimeSeriesPlot(df, …) - Plotting ggplot-based objects with combinations of variables, time intervals, and aggregation functions Time Series Digger : Automatic time series analysis for data science in R Motoyuki OKI, Yusuke SAITO, Yuki HIRA, and Yukio UEMATSU; NTT Communications corp.; E-mail : dstu-td@ntt.com Introduction Time Series Digger and Real-World Usecase Exploratory Data Analysis Feature Construction for Achieving High Detection Accuracy Task Explore useful variables and time intervals to detect anomalies addDatetimeExpression(df, …) -Creating multiple time expression addBasicStatistics(df, …) -Creating descriptive statistics features based on multiple moving functions A great number of series by time intervals and variables L Automatic plot multiple variables, time intervals and distributions J Find useful intervals to detection Short Time Interval Long df Time series oriented feature extraction L Task Construct useful time series oriented features Motivation Developing Time Series Digger to accelerate the process Modeling for Anomaly Detection Various methods and packages with different interface L Task Detect anomaly of time point t from past sub time series features dfExample Time Features Moving Average Features AnomalyDetection(df, method, …) -Detecting anomalies based on Singular Spectrum Transformation, Robust Principal Component Analysis, and other methods Problem Settings Data Science Process 1 - 10 million records /day 100 thousand records /day 10 - 100 million records /day Exploratory Data Analysis Feature Construction Modeling Network data Singular Spectrum Transformation Robust Principal Component Analysis Evaluation Different purposes with multiple datasets Discussion Setting suitable metrics for the purposes

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