A fully nonparametric MDL-based framework jointly infers contiguous spatial regions and temporal archetypes from time series in log-linear time.
In practice, the appropriate number of regions is rarely known in advance and may vary with temporal resolution, noise level, and time series length
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Scalable inference of spatial regions and temporal signatures from time series
A fully nonparametric MDL-based framework jointly infers contiguous spatial regions and temporal archetypes from time series in log-linear time.