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Appendix A: An Overview on Time Series Data Mining

Appendix A: An Overview on Time Series Data Mining includes Introduction, Similarity Search in Time Series Data, Feature-based Dimensionality Reduction, Discretization, Other Time Series Data Mining Tasks, Conclusions. | Appendix A: An Overview on Time Series Data Mining Duong Tuan Anh Faculty of Computer Science & Engineering Ho Chi Minh City University of Technology October 2009 Course: Decision Support Systems An Overview on Time series Data Mining Outline Introduction Similarity Search in Time Series Data Feature-based Dimensionality Reduction Discretization Other Time Series Data Mining Tasks Conclusions Introduction 0 50 100 150 200 250 300 350 400 450 500 23 24 25 26 27 28 29 25.1750 25.2250 25.2500 25.2500 25.2750 25.3250 25.3500 25.3500 25.4000 25.4000 25.3250 25.2250 25.2000 25.1750 24.6250 24.6750 24.6750 24.6250 24.6250 24.6250 24.6750 24.7500 A time series is a collection of observations made sequentially in time. Examples: Financial time series, scientific time series Time series Similarity Search Some examples: - Identifying companies with similar patterns of growth. - Determining products with similar selling patterns - Discovering stocks with similar movement in stock prices. - Finding out whether a musical score is similar to one of a set of copyrighted scores. Time series Similarity Search Distance Measures Euclidean distance Dynamic Time Warping Other distance measures Euclidean Distance Metric Given two time series Q = q1 qn and C = c1 cn their Euclidean distance is defined as: Q C D(Q,C) Fixed Time Axis Sequences are aligned “one to one”. “Warped” Time Axis Nonlinear alignments are possible. Dynamic Time Warping (Berndt et al.) Dynamic Time Warping is a technique that finds the optimal alignment between two time series if one time series may be “warped” non-linearly by stretching or shrinking it along its time axis. This warping between two time series can be used or to determine the similarity between the two time series. Dynamic Time Warping DTW facilitates the discovery of flexible patterns from time series. DTW is used in speech regcognition to determine if two waveforms represent the same spoken phrase. Disadvantage: . | Appendix A: An Overview on Time Series Data Mining Duong Tuan Anh Faculty of Computer Science & Engineering Ho Chi Minh City University of Technology October 2009 Course: Decision Support Systems An Overview on Time series Data Mining Outline Introduction Similarity Search in Time Series Data Feature-based Dimensionality Reduction Discretization Other Time Series Data Mining Tasks Conclusions Introduction 0 50 100 150 200 250 300 350 400 450 500 23 24 25 26 27 28 29 25.1750 25.2250 25.2500 25.2500 25.2750 25.3250 25.3500 25.3500 25.4000 25.4000 25.3250 25.2250 25.2000 25.1750 24.6250 24.6750 24.6750 24.6250 24.6250 24.6250 24.6750 24.7500 A time series is a collection of observations made sequentially in time. Examples: Financial time series, scientific time series Time series Similarity Search Some examples: - Identifying companies with similar patterns of growth. - Determining products with similar selling patterns - Discovering stocks with similar movement in .