Endometrial timing methods based on gene expression that were commercially accessible required large gene sets and used a categorical approach that categorized samples as pre-receptive, receptive, or post-receptive. About 664 endometrial biopsies were taken 4–12 days following a self-reported positive home ovulation test, and gene expression was evaluated by RTq-PCR in various sample sets. In addition, RTq-PCR and RNA-sequencing were used to profile another 36 endometrial samples. The ‘EndoTime’ computational approach was developed to simulate the temporal profile of each gene and estimate the timing of each piece. As sample timings were updated and confidence in timing estimations grew, temporal profiles were gradually refined as the procedures were repeated. Following convergence, the approach updated each sample’s timing estimates while maintaining the overall distribution of time points. Ordering samples using EndoTime estimates gives crisper temporal expression profiles for held-out genes (not employed for computing sample timings) than ordering the same expression values by patient-reported times (GPX3: P<0.005; CXCL14: P<2.7e6; DPP4: P<3.7e13). The Pearson correlation between EndoTime estimations based on RTq-PCR or RNA-sequencing data for the same sample set indicated a significant congruency (P=8.6e10, R2=0.687). Control persons and patients with repeated pregnancy loss or recurrent implantation failure did not have substantially different estimated timeframes (P>0.05).