Abstract: Anomaly detection is vital for spacecraft health in orbit. Existing algorithms rely on high-quality training data and suffer marked performance loss on typically incomplete spacecraft data. This study examines the impact of incomplete spacecraft data on detection performance. First, three granular levels of missingness metrics are proposed based on missing values' effect on model inputs. Then, a generalization performance measure using these metrics is designed, and an upper bound for detection model degradation is theoretically proven. Finally, the prediction algorithm ADForecast is developed from this measure; it accurately forecasts detection accuracy at a given missing ratio and integrates with various detection algorithms. Experiments show ADForecast efficiently and accurately predicts the performance of multiple algorithms under most missing ratios; when pretrained with historical data from similar spacecraft, the prediction error stays below 5%.