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» Discovering Temporal Knowledge in Multivariate Time Series
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KDD
2006
ACM
228views Data Mining» more  KDD 2006»
14 years 7 months ago
Algorithms for time series knowledge mining
Temporal patterns composed of symbolic intervals are commonly formulated with Allen's interval relations originating in temporal reasoning. This representation has severe dis...
Fabian Mörchen
SC
2009
ACM
14 years 2 months ago
Terascale data organization for discovering multivariate climatic trends
Current visualization tools lack the ability to perform fullrange spatial and temporal analysis on terascale scientific datasets. Two key reasons exist for this shortcoming: I/O ...
Wesley Kendall, Markus Glatter, Jian Huang, Tom Pe...
IJCNN
2007
IEEE
14 years 1 months ago
Neural Network Ensembles for Time Series Prediction
— Rapidly evolving businesses generate massive amounts of time-stamped data sequences and defy a demand for massively multivariate time series analysis. For such data the predict...
Dymitr Ruta, Bogdan Gabrys
GFKL
2005
Springer
133views Data Mining» more  GFKL 2005»
14 years 1 months ago
Finding Persisting States for Knowledge Discovery in Time Series
Abstract. Knowledge Discovery in time series usually requires symbolic time series. Many discretization methods that convert numeric time series to symbolic time series ignore the ...
Fabian Mörchen, Alfred Ultsch
SDM
2012
SIAM
355views Data Mining» more  SDM 2012»
11 years 10 months ago
Granger Causality Analysis in Irregular Time Series
Learning temporal causal structures between time series is one of the key tools for analyzing time series data. In many real-world applications, we are confronted with Irregular T...
Mohammad Taha Bahadori, Yan Liu