The increasing appreciation for the human gut microbiome potential roles in diagnosis, treatment, and ultimately prevention of human disease requires an understanding of the temporal variability of the microbial community over the lifespan of an individual. Given the highly dynamic and complex nature of the human gut microbial community, the ability to di↵erentiate between the OTUs behavior over time, and specifically, identify and predict the time-dependent OTUs, is crucial. However, the field has not yet settled upon the degree to which the microbial composition of the gut at a given time a↵ects the microbial composition at a later time. Particularly, it has been recently suggested that only a minority of the OTUs depend on the microbial composition in earlier times. Here, we demonstrate that these earlier findings are severely biased by the sub-optimality of the statistical approaches used to model the microbial composition dependency in time. To that end, we introduce MTV-LMM , a linear mixed model method for the prediction of the microbial community temporal dynamics based on the community composition at previous time stamps. MTV-LMM thus allows identifying auto-regressive OTUs in time series microbiome datasets, which can be taken to further analysis of the microbiome trajectory over time. We evaluated the performance of MTV-LMM on three microbiome time series datasets, and we found that MTV-LMM significantly outperforms the existing methods for microbiome time-series modeling. Furthermore, we quantify the time-dependency of the microbiome in infants and adults and we demonstrate, for the first time, that a considerable proportion of OTUs has a significant auto-regressive component, in both infants and adults, and that time-dependency is a considerably more abundant phenomenon compared to previous report.peer-reviewed)