Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
The power of amnesia: learning probabilistic automata with variable memory length
Machine Learning - Special issue on COLT '94
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Cluster and Calendar Based Visualization of Time Series Data
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Suppressing model overfitting in mining concept-drifting data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A decade of progress in indexing and mining large time series databases
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Mining Complex Time-Series Data by Learning Markovian Models
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Effective variation management for pseudo periodical streams
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
A second-order HMM for high performance word and phoneme-based continuous speech recognition
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Proceedings of the VLDB Endowment
Stop Chasing Trends: Discovering High Order Models in Evolving Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Online Anomaly Prediction for Robust Cluster Systems
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Concept Clustering of Evolving Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Finding Time Series Motifs in Disk-Resident Data
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Managing massive time series streams with multi-scale compressed trickles
Proceedings of the VLDB Endowment
An algorithmic approach to event summarization
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Adaptive system anomaly prediction for large-scale hosting infrastructures
Proceedings of the 29th ACM SIGACT-SIGOPS symposium on Principles of distributed computing
Online discovery and maintenance of time series motifs
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining effective multi-segment sliding window for pathogen incidence rate prediction
Data & Knowledge Engineering
Travel cost inference from sparse, spatio temporally correlated time series using Markov models
Proceedings of the VLDB Endowment
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In order to understand a complex system, we analyze its output or its log data. For example, we track a system's resource consumption (CPU, memory, message queues of different types, etc) to help avert system failures; we examine economic indicators to assess the severity of a recession; we monitor a patient's heart rate or EEG for disease diagnosis. Time series data is involved in many such applications. Much work has been devoted to pattern discovery from time series data, but not much has attempted to use the time series data to unveil a system's internal dynamics. In this paper, we go beyond learning patterns from time series data. We focus on obtaining a better understanding of its data generating mechanism, and we regard patterns and their temporal relations as organic components of the hidden mechanism. Specifically, we propose to model time series data using a novel pattern-based hidden Markov model (pHMM), which aims at revealing a global picture of the system that generates the time series data. We propose an iterative approach to refine pHMMs learned from the data. In each iteration, we use the current pHMM to guide time series segmentation and clustering, which enables us to learn a more accurate pHMM. Furthermore, we propose three pruning strategies to speed up the refinement process. Empirical results on real datasets demonstrate the feasibility and effectiveness of the proposed approach.