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Found 3 papers, 2 papers with code
Some Developments in Clustering Analysis on Stochastic Processes
We review some developments on clustering stochastic processes and come with the conclusion that asymptotically consistent clustering algorithms can be obtained when the processes are ergodic and the dissimilarity measure satisfies the triangle inequality.
Cluster Analysis on Locally Asymptotically Self-similar Processes with Known Number of Clusters
We conduct cluster analysis on a class of locally asymptotically self-similar stochastic processes, which includes multifractional Brownian motion as a representative.
Covariance-based Dissimilarity Measures Applied to Clustering Wide-sense Stationary Ergodic Processes
We introduce a new unsupervised learning problem: clustering wide-sense stationary ergodic stochastic processes.
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