An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Comparing the similarity of time-series gene expression using signal processing metrics
Computers and Biomedical Research
Mining Similar Temporal Patterns in Long Time-Series Data and Its Application to Medicine
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Clustering of streaming time series is meaningless
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Chaos and Time-Series Analysis
Chaos and Time-Series Analysis
Visually mining and monitoring massive time series
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning States and Rules for Detecting Anomalies in Time Series
Applied Intelligence
Making Subsequence Time Series Clustering Meaningful
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
Knowledge construction from time series data using a collaborative exploration system
Journal of Biomedical Informatics
Precedence Temporal Networks to represent temporal relationships in gene expression data
Journal of Biomedical Informatics
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 05
Temporal data mining for the quality assessment of hemodialysis services
Artificial Intelligence in Medicine
Computerised anaesthesia monitoring using fuzzy trend templates
Artificial Intelligence in Medicine
Mining latent sources of causal time series using nonlinear state space modeling
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
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Time series estimation techniques are usually employed in biomedical research to derive variables less accessible from a set of related and more accessible variables. These techniques are traditionally built from systems modeling approaches including simulation, blind decovolution, and state estimation. In this work, we define target time series (TTS) and its related time series (RTS) as the output and input of a time series estimation process, respectively. We then propose a novel data mining framework for time series estimation when TTS and RTS represent different sets of observed variables from the same dynamic system. This is made possible by mining a database of instances of TTS, its simultaneously recorded RTS, and the input/output dynamic models between them. The key mining strategy is to formulate a mapping function for each TTS-RTS pair in the database that translates a feature vector extracted from RTS to the dissimilarity between true TTS and its estimate from the dynamic model associated with the same TTS-RTS pair. At run time, a feature vector is extracted from an inquiry RTS and supplied to the mapping function associated with each TTS-RTS pair to calculate a dissimilarity measure. An optimal TTS-RTS pair is then selected by analyzing these dissimilarity measures. The associated input/output model of the selected TTS-RTS pair is then used to simulate the TTS given the inquiry RTS as an input. An exemplary implementation was built to address a biomedical problem of noninvasive intracranial pressure assessment. The performance of the proposed method was superior to that of a simple training-free approach of finding the optimal TTS-RTS pair by a conventional similarity-based search on RTS features.