This article proposes a new decision tree algorithm that accounts for time-varying covariates in the decision-making process. Traditional decision tree algorithms assume that the covariates are static and do not change over time, which can lead to inaccurate predictions in dynamic environments. Other existing methods suggest workaround solutions such as the pseudo-subject approach, discussed in the article. The proposed algorithm utilizes a different structure and a time-penalized splitting criterion that allow a recursive partitioning of both the covariates space and time. Relevant historical trends are then inherently involved in the construction of a tree, and are visible and interpretable once it is fit. This approach allows for innovative and highly interpretable analysis in settings where the covariates are subject to change over time. The effectiveness of the algorithm is demonstrated through real-world data analysis, highlighting its potential applications in various fields, including healthcare, finance, insurance, environmental monitoring, and data mining in general.