Data mining: concepts and techniques
Data mining: concepts and techniques
Introduction to Algorithms
Properties of Embedding Methods for Similarity Searching in Metric Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
PERUSE: An Unsupervised Algorithm for Finding Recurrig Patterns in Time Series
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Data Mining and Knowledge Discovery
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A Subsequence Matching Algorithm that Supports Normalization Transform in Time-Series Databases
Data Mining and Knowledge Discovery
Clustering of time-series subsequences is meaningless: implications for previous and future research
Knowledge and Information Systems
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Discovering multivariate motifs using subsequence density estimation and greedy mixture learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Toward unsupervised activity discovery using multi-dimensional motif detection in time series
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Mining multiple time series co-movements
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
Database research at the National University of Singapore
ACM SIGMOD Record
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Time series motif discovery is important as the discovered motifs generally form the primitives for many data mining tasks. In this work, we examine the problem of discovering groups of motifs from different time series that exhibit some lag relationships. We define a new class of pattern called lagPatterns that captures the invariant ordering among motifs. lagPatterns characterize localized associative pattern involving motifs derived from each entity and explicitly accounts for lag across multiple entities. We present an exact algorithm that makes use of the order line concept and the subsequence matching property of the normalized time series to find all motifs of various lengths. We also describe a method called LPMiner to discover lagPatterns efficiently. LPMiner utilizes inverted index and motif alignment technique to reduce the search space and improve the efficiency. A detailed empirical study on synthetic datasets shows the scalability of the proposed approach. We show the usefulness of lagPatterns discovered from a stock dataset by constructing stock portfolio that leads to a higher cumulative rate of return on investment.