  This paper proposes schemes for automated and weighted Self-Organizing Time
Maps (SOTMs). The SOTM provides means for a visual approach to evolutionary
clustering, which aims at producing a sequence of clustering solutions. This
task we denote as visual dynamic clustering. The implication of an automated
SOTM is not only a data-driven parametrization of the SOTM, but also the
feature of adjusting the training to the characteristics of the data at each
time step. The aim of the weighted SOTM is to improve learning from more
trustworthy or important data with an instance-varying weight. The schemes for
automated and weighted SOTMs are illustrated on two real-world datasets: (i)
country-level risk indicators to measure the evolution of global imbalances,
and (ii) credit applicant data to measure the evolution of firm-level credit
risks.
