Seafloor massive sulfide deposits are attracting attention as new sources of industrially important metals. Methods for detecting these deposits using a multi-beam echo sounder (MBES) was previously demonstrated, with recent interest in automating this detection using object detection models. However, conventional models often produce numerous false positives due to the sparse occurrence of hydrothermal signals during actual explorations. Here, we report that by incorporating a simple timeseries analysis into the outputs of conventional object detection models, we have, for the first time, successfully achieved highprecision detection of hydrothermal signatures from MBES images acquired by autonomous underwater vehicles (AUVs). An object detection model, YOLOv8, was utilized to detect hydrothermal signals from time-series MBES data obtained by AUVs. A 5-term moving average was applied to the confidence scores, followed by binary classification to determine the presence of hydrothermal signals in each frame. Cross-validation using data from 13 dives showed an improvement in precision from 0.425-0.582 at the frame and 0.142-0.541 at the hydrothermal event levels. Additionally, an evaluation method considering practical aspects of hydrothermal exploration, such as deployment frequency and discovery rates, was developed. This method more accurately assesses the practical utility of hydrothermal exploration. The results indicate that the proposed algorithm, leveraging time-series information through a moving average, is potentially effective for practical hydrothermal exploration.