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Time‐series clustering typically entails clustering similar patterns across various time scales or comparing various point trajectories. However, this study emphasizes to group data points based on their motions and forecast how the clusters will evolve across a number of immediate time frames. To achieve this, we propose a DYNamic Aggregation of Mutually‐connected poInts clusTEring (DYNAMITE) based clustering algorithm for time series. DYNAMITE is based on the interaction between points in a time series and it majorly consists of three components: (1) cluster initialization; (2) calculation of mutually connected points; and (3) cluster updating.

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