Similarity searching in sequence databases under time warping.

Wong, Siu Fung.Thesis submitted in: December 2003.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 77-84).Abstracts in English and Chinese.Abstract --- p.iiAcknowledgement --- p.viChapter 1 --- Introduction --- p.1Chapter 2 --- Preliminary --- p.6Chapter 2.1 --- Dynamic Time Warping (DTW) --- p.6Chapter 2.2 --- Spatial Indexing --- p.10Chapter 2.3 --- Relevance Feedback --- p.11Chapter 3 --- Literature Review --- p.13Chapter 3.1 --- Searching Sequences under Euclidean Metric --- p.13Chapter 3.2 --- Searching Sequences under Dynamic Time Warping Metric --- p.17Chapter 4 --- Subsequence Matching under Time Warping --- p.21Chapter 4.1 --- Subsequence Matching --- p.22Chapter 4.1.1 --- Sequential Search --- p.22Chapter 4.1.2 --- Indexing Scheme --- p.23Chapter 4.2 --- Lower Bound Technique --- p.25Chapter 4.2.1 --- Properties of Lower Bound Technique --- p.26Chapter 4.2.2 --- Existing Lower Bound Functions --- p.27Chapter 4.3 --- Point-Based indexing --- p.28Chapter 4.3.1 --- Lower Bound for subsequences matching --- p.28Chapter 4.3.2 --- Algorithm --- p.35Chapter 4.4 --- Rectangle-Based indexing --- p.37Chapter 4.4.1 --- Lower Bound for subsequences matching --- p.37Chapter 4.4.2 --- Algorithm --- p.41Chapter 4.5 --- Experimental Results --- p.43Chapter 4.5.1 --- Candidate ratio vs Width of warping window --- p.44Chapter 4.5.2 --- CPU time vs Number of subsequences --- p.45Chapter 4.5.3 --- CPU time vs Width of warping window --- p.46Chapter 4.5.4 --- CPU time vs Threshold --- p.46Chapter 4.6 --- Summary --- p.47Chapter 5 --- Relevance Feedback under Time Warping --- p.49Chapter 5.1 --- Integrating Relevance Feedback with DTW --- p.49Chapter 5.2 --- Query Reformulation --- p.53Chapter 5.2.1 --- Constraint Updating --- p.53Chapter 5.2.2 --- Weight Updating --- p.55Chapter 5.2.3 --- Overall Strategy --- p.58Chapter 5.3 --- Experiments and Evaluation --- p.59Chapter 5.3.1 --- Effectiveness of the strategy --- p.61Chapter 5.3.2 --- Efficiency of the strategy --- p.63Chapter 5.3.3 --- Usability --- p.64Chapter 5.4 --- Summary --- p.71Chapter 6 --- Conclusion --- p.72Chapter A --- Deduction of Data Bounding Hyper-rectangle --- p.74Chapter B --- Proof of Theorem2 --- p.76Bibliography --- p.77Publications --- p.8

Efficient time series matching by wavelets.

by Chan, Kin Pong.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 100-105).Abstracts in English and Chinese.Acknowledgments --- p.iiAbstract --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Wavelet Transform --- p.4Chapter 1.2 --- Time Warping --- p.5Chapter 1.3 --- Outline of the Thesis --- p.6Chapter 2 --- Related Work --- p.8Chapter 2.1 --- Similarity Models for Time Series --- p.8Chapter 2.2 --- Dimensionality Reduction --- p.11Chapter 2.3 --- Wavelet Transform --- p.15Chapter 2.4 --- Similarity Search under Time Warping --- p.16Chapter 3 --- Dimension Reduction by Wavelets --- p.21Chapter 3.1 --- The Proposed Approach --- p.21Chapter 3.1.1 --- Haar Wavelets --- p.23Chapter 3.1.2 --- DFT versus Haar Transform --- p.27Chapter 3.1.3 --- Guarantee of no False Dismissal --- p.29Chapter 3.2 --- The Overall Strategy --- p.34Chapter 3.2.1 --- Pre-processing --- p.35Chapter 3.2.2 --- Range Query --- p.35Chapter 3.2.3 --- Nearest Neighbor Query --- p.36Chapter 3.3 --- Performance Evaluation --- p.39Chapter 3.3.1 --- Stock Data --- p.39Chapter 3.3.2 --- Synthetic Random Walk Data --- p.45Chapter 3.3.3 --- Scalability Test --- p.51Chapter 3.3.4 --- Other Wavelets --- p.52Chapter 4 --- Time Warping --- p.55Chapter 4.1 --- Similarity Search based on K-L Transform --- p.60Chapter 4.2 --- Low Resolution Time Warping --- p.63Chapter 4.2.1 --- Resolution Reduction of Sequences --- p.63Chapter 4.2.2 --- Distance Compensation --- p.67Chapter 4.2.3 --- Time Complexity --- p.73Chapter 4.3 --- Adaptive Time Warping --- p.77Chapter 4.3.1 --- Time Complexity --- p.79Chapter 4.4 --- Performance Evaluation --- p.80Chapter 4.4.1 --- Accuracy versus Runtime --- p.80Chapter 4.4.2 --- Precision versus Recall --- p.85Chapter 4.4.3 --- Overall Runtime --- p.91Chapter 4.4.4 --- Starting Up Evaluation --- p.93Chapter 5 --- Conclusion and Future Work --- p.95Chapter 5.1 --- Conclusion --- p.95Chapter 5.2 --- Future Work --- p.96Chapter 5.2.1 --- Application of Wavelets on Biomedical Signals --- p.96Chapter 5.2.2 --- Moving Average Similarity --- p.98Chapter 5.2.3 --- Clusters-based Matching in Time Warping --- p.98Bibliography --- p.9

Feature extraction and pattern matching in time series data.

Wan Po Man Polly.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 122-128).Abstracts in English and Chinese.Abstract --- p.iAcknowledgements --- p.vContents --- p.viList of Figures --- p.xList of Tables --- p.xivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation and Aims --- p.1Chapter 1.2 --- Organization of Thesis --- p.5Chapter 2 --- Literature Review --- p.6Chapter 2.1 --- Dimensionality Reduction --- p.6Chapter 2.1.1 --- Fourier Transformation --- p.6Chapter 2.1.2 --- Wavelet Transformation --- p.8Chapter 2.1.3 --- Singular Value Decomposition --- p.10Chapter 2.2 --- Searching Sequence Similarity with Transformation --- p.11Chapter 2.2.1 --- Time Warping --- p.11Chapter 2.2.2 --- Amplitude Scaling and Shifting --- p.14Chapter 2.3 --- Data Smoothing and Noise Removal --- p.18Chapter 2.3.1 --- Piecewise Linear Segmentations --- p.18Chapter 2.3.2 --- Approximation Function --- p.21Chapter 2.3.3 --- Best-fitting Line --- p.23Chapter 2.3.4 --- Turning Points --- p.24Chapter 3 --- Time-Series Searching with Scaling and Shifting in Amplitude and Time Domains --- p.25Chapter 3.1 --- Representation --- p.25Chapter 3.1.1 --- Control Points --- p.26Chapter 3.1.2 --- Lattice Structure --- p.28Chapter 3.1.3 --- Algorithm on Lattice Construction --- p.31Chapter 3.2 --- Pattern Matching --- p.32Chapter 3.2.1 --- Formulating the Problem of Similarity --- p.35Chapter 3.2.2 --- Error Measurement --- p.38Chapter 3.3 --- Indexing Scheme --- p.39Chapter 3.3.1 --- Indexing with scaling and shifting proposed by Chu and Wong --- p.40Chapter 3.3.2 --- Integrating with lattice structure --- p.41Chapter 3.4 --- Results --- p.43Chapter 4 --- Chart Patterns Searching for Chart Analysis --- p.47Chapter 4.1 --- Chart Patterns Overview --- p.47Chapter 4.1.1 --- Reversal Patterns --- p.49Chapter 4.1.2 --- Continuation Patterns --- p.52Chapter 4.2 --- Representation --- p.53Chapter 4.2.1 --- Trendline Preparation --- p.54Chapter 4.2.2 --- Trendline Pair --- p.59Chapter 4.3 --- Three-Phase Pattern Classification --- p.66Chapter 4.3.1 --- Phase One: Trendline Pair Classification --- p.66Chapter 4.3.2 --- Phase Two: Patterns Merging and Rejection --- p.74Chapter 4.3.3 --- Phase Three: Patterns Merging of Unclassified and Un- merged Trendline Pairs --- p.89Chapter 4.4 --- Results --- p.90Chapter 5 --- Conclusion --- p.100Chapter A --- Supplementary Results --- p.103Chapter A.1 --- Ascending Triangle --- p.103Chapter A.2 --- Descending Triangle --- p.104Chapter A.3 --- Falling Wedge --- p.106Chapter A.4 --- Head and Shoulders --- p.107Chapter A.5 --- Price Channel --- p.109Chapter A.6 --- Rectangle --- p.110Chapter A.7 --- Rising Wedge --- p.112Chapter A.8 --- Symmetric Triangle --- p.113Chapter A.9 --- Double Bottom --- p.113Chapter A.10 --- Double Top --- p.116Chapter A.11 --- Triple Bottom --- p.118Chapter A.12 --- Triple Top --- p.120Bibliography --- p.122Publications --- p.12