Aircraft telemetry data are the only source for ground-based assessment of satellite in-orbit status. Anomaly detection facilitates condition-based dynamic decision-making during aircraft operations and effectively reduces failures. However, existing methods primarily focus on short-term variations, making it difficult to identify collective anomaly patterns effectively. To address this issue, this article proposes a collective anomaly detection method for aircraft telemetry data based on long timescale characteristic modeling optimization. First, a temporal correlation model is formulated to extract high-dimensional patterns from telemetry data segments and generate prediction results. Then, using the residuals between the prediction results and observed data, a statistical model is developed to extract distribution characteristics and establish anomaly detection criteria. Finally, iterative prediction is employed to automatically adjust model inputs, enhancing the robustness of collective anomaly detection. Validation using actual aircraft attitude angle telemetry data shows that, compared with the VAE-LSTM model, the proposed method improves the detection rate of anomaly segments by 0.041 and the F1 score by 0.039. These results show the method′s advantages in improving detection accuracy and reducing missed detections, providing reliable data support for condition-based satellite operations and maintenance.