Graphs and algorithms
Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Integer and combinatorial optimization
Integer and combinatorial optimization
Automatic recognition of primitive changes in manufacturing process signals
Pattern Recognition
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Time-Series Segmentation and Symbolic Representation, from Process-Monitoring to Data-Mining
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Dynamic Programming
Journal of Computer and System Sciences
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Optimum curve segmentation problems typically arise when analyzing data represented by curves or graphs of real-valued functions in one real variable. Examples of applications are many, including: *time series analysis and forecasting; *analysis and identification of dynamical systems; *process-monitoring (analysis and modelling of input-output relationships for physical systems such as chemical reactors, engines, electrical systems, biological systems, etc.). We propose here a general framework for stating and solving such problems, either exactly or approximately, using polynomial approximation schemes. Both the discrete version of the problem (Discrete Segmentation Problem, DSP) and the continuous version of the problem (Continuous Segmentation Problem, CSP) are addressed. We investigate various sets of conditions under which DSP or CSP can be solved either exactly in polynomial time or approximately by means of a fully polynomial-time approximation scheme (FPTAS). Finally, we formulate the discrete segmentation problem with variable number of segments (DSPV) and show that it can be formulated as an integer linear program reducible to minimum cost network flow and shortest path computations.