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» Subsequence Matching on Structured Time Series Data
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NIPS
1998
13 years 9 months ago
Coding Time-Varying Signals Using Sparse, Shift-Invariant Representations
A common way to represent a time series is to divide it into shortduration blocks, each of which is then represented by a set of basis functions. A limitation of this approach, ho...
Michael S. Lewicki, Terrence J. Sejnowski
VLDB
2005
ACM
151views Database» more  VLDB 2005»
14 years 1 months ago
On Map-Matching Vehicle Tracking Data
Vehicle tracking data is an essential “raw” material for a broad range of applications such as traffic management and control, routing, and navigation. An important issue with...
Sotiris Brakatsoulas, Dieter Pfoser, Randall Salas...
PKDD
2007
Springer
109views Data Mining» more  PKDD 2007»
14 years 2 months ago
Matching Partitions over Time to Reliably Capture Local Clusters in Noisy Domains
Abstract. When seeking for small clusters it is very intricate to distinguish between incidental agglomeration of noisy points and true local patterns. We present the PAMALOC algor...
Frank Höppner, Mirko Böttcher
BMCBI
2007
215views more  BMCBI 2007»
13 years 8 months ago
Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregres
Background: Causal networks based on the vector autoregressive (VAR) process are a promising statistical tool for modeling regulatory interactions in a cell. However, learning the...
Rainer Opgen-Rhein, Korbinian Strimmer
ICML
2010
IEEE
13 years 7 months ago
Heterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-Time Segment Information Sharing
Classical dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption and cannot deal with heterogeneity and non-stationarity in temporal processes. Various ap...
Frank Dondelinger, Sophie Lebre, Dirk Husmeier