Identifying distinctive subsequences in multivariate time series by clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Similarity Searching for Multi-Attribute Sequences
SSDBM '02 Proceedings of the 14th International Conference on Scientific and Statistical Database Management
Tree Induction for Probability-Based Ranking
Machine Learning
A Signature Technique for Similarity-Based Queries
SEQUENCES '97 Proceedings of the Compression and Complexity of Sequences 1997
Similarity Search for Multidimensional Data Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Indexing Multidimensional Time-Series
The VLDB Journal — The International Journal on Very Large Data Bases
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Optimistic pruning for multiple instance learning
Pattern Recognition Letters
Semi-supervised learning with data calibration for long-term time series forecasting
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Real-Time Classification of Streaming Sensor Data
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Spatiotemporal Relational Probability Trees: An Introduction
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Clustering Distributed Time Series in Sensor Networks
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Time series shapelets: a new primitive for data mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
OPUS: an efficient admissible algorithm for unordered search
Journal of Artificial Intelligence Research
Spatiotemporal Relational Random Forests
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Why does subsequence time-series clustering produce sine waves?
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Towards generic pattern mining
ICFCA'05 Proceedings of the Third international conference on Formal Concept Analysis
Logical-shapelets: an expressive primitive for time series classification
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Correlation based dynamic time warping of multivariate time series
Expert Systems with Applications: An International Journal
Enhanced spatiotemporal relational probability trees and forests
Data Mining and Knowledge Discovery
Multi-dimensional pattern discovery in financial time series using sax-ga with extended robustness
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Social life networks: a multimedia problem?
Proceedings of the 21st ACM international conference on Multimedia
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We introduce an efficient approach to mining multi-dimensional temporal streams of real-world data for ordered temporal motifs that can be used for prediction. Since many of the dimensions of the data are known or suspected to be irrelevant, our approach first identifies the salient dimensions of the data, then the key temporal motifs within each dimension, and finally the temporal ordering of the motifs necessary for prediction. For the prediction element, the data are assumed to be labeled. We tested the approach on two real-world data sets. To verify the generality of the approach, we validated the application on several subjects from the CMU Motion Capture database. Our main application uses several hundred numerically simulated supercell thunderstorms where the goal is to identify the most important features and feature interrelationships which herald the development of strong rotation in the lowest altitudes of a storm. We identified sets of precursors, in the form of meteorological quantities reaching extreme values in a particular temporal sequence, unique to storms producing strong low-altitude rotation. The eventual goal is to use this knowledge for future severe weather detection and prediction algorithms.