Data attribution methods often compress the effect of each training data point into a single score, computed either at a final checkpoint or by aggregating quantities along the training trajectory. This static view hides how the contribution of a training data point changes during training. To overcome this limitation, we propose the Window-level Influence Estimator (WIE), a framework for estimating the contribution of a training data point zⱼ over a user-specified training window [t₁, t₂]. WIE models a leave-one-out-style counterfactual under a first-order linearization of SGD. An influence propagator P[t] = I − ηₜH[t] at each step models how parameter deviation is amplified or dampened by the loss landscape. Empirically, the window-level estimator reveals four distinct influence patterns: Early Influencer, Late Bloomer, Stable, Highly Fluctuating. Across multiple benchmarks, WIE improves mislabeled-data detection over the considered static and trajectory-based baselines. On BERT-IMDB with 40% label noise, WIE outperforms these baselines across the evaluated inspection ratios. On ViT-EMNIST, WIE reaches 70% recall for mislabeled data while inspecting up to 7.5× fewer candidates than TracIn requires to reach the same recall.