# Efficient Query Filtering as long as Streaming Time Series Outline of Talk What are Time Series Time Series are Everywhere Time Series Data Mining Tasks

## Efficient Query Filtering as long as Streaming Time Series Outline of Talk What are Time Series Time Series are Everywhere Time Series Data Mining Tasks

Kovacevic, Katarina, Freelance Writer has reference to this Academic Journal, PHwiki organized this Journal Efficient Query Filtering as long as Streaming Time Series ICDM ’05 Outline of Talk Introduction to time series Time series filtering Wedge-based approach Experimental results Conclusions What are Time Series Time series are collections of observations made sequentially in time. 4.7275 4.7083 4.6700 4.6600 4.6617 4.6517 4.6500 4.6500 4.6917 4.7533 4.8233 4.8700 4.8783 4.8700 4.8500 4.8433 4.8383 4.8400 4.8433

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Time Series are Everywhere ECG Heartbeat Image Stock Video Time Series Data Mining Tasks Clustering Classification Query by Content Rule Discovery s = 0.5 c = 0.3 Motif Discovery Anomaly Detection Visualization 10 2 1 4 3 7 6 5 9 8 10 11 12 C in addition to idates Time Series Filtering Given a Time Series T, a set of C in addition to idates C in addition to a distance threshold r, find all subsequences in T that are within r distance to any of the c in addition to idates in C. Matches Q11 Time Series

2 1 4 3 7 6 5 9 8 10 11 12 Queries Matches Q11 Database Database Query (template) 2 1 4 3 5 7 6 9 8 10 Database Best match Filtering vs. Querying Euclidean Distance Metric Given two time series Q = q1 qn in addition to C = c1 cn , the Euclidean distance between them is defined as: Early Ab in addition to on During the computation, if current sum of the squared differences between each pair of corresponding data points exceeds r 2, we can safely stop the calculation.

2 1 4 3 7 6 5 9 8 10 11 12 C in addition to idates Wedge Based Approach Compare the query to the wedge using LB-Keogh If the LB-Keogh function early ab in addition to ons, we are done Otherwise individually compare each c in addition to idate sequences to the query using the early ab in addition to oning algorithm Time Series Examples of Wedge Merging W((1, 2), 3) Hierarchal Clustering C1 (or W1) C4 (or W4) C2 (or W2) C5 (or W5) C3 (or W3) Which wedge set to choose