## 4 Conclusions  
The traditional method of early warning through precursor recognition typically offers limited lead time, usually within 7 days. Prolonged precursor signals can delay warnings, posing challenges for timely intervention. Some researchers have utilized recurrent neural  
Figure 11: Comparison chart of raw data and CWT time–frequency data.  
Figure 12: The framework’s super prediction and precursor characteristics recognition results.  
networks (RNNs) and transformer-based architectures for time-series prediction, achieving FEMR signal forecasting up to 7 days in advance. While effective for short-term predictions, this approach necessitates multi-source data fusion and extensive sensor deployments to ensure a high accuracy, relying on substantial pre-existing datasets. This requirement imposes significant storage burdens on coal mine databases, a persistent challenge in operational contexts.  
In contrast, this study introduces an innovative approach leveraging a guided diffusion model coupled with a transformer-based data restoration architecture for the processing of known FEMR signals. This model facilitates the super prediction of FEMR data, forecasting up to 360 h (15 days) into the future based solely on 12 h of known data. Additionally, an auxiliary model is employed to identify rock burst precursor characteristics within the FEMR data. The empirical results underscore the efficacy of this approach in predicting FEMR signals over extended time periods, while also conducting a comprehensive analysis of the upward trends inherent in FEMR signals--key indicators of rock burst precursors.  
The academic contribution of this method lies in its ability to extend prediction horizons and enhance the interpretability of rock burst precursor identification. With an accuracy rate of 98.07%, the approach significantly improves the reliability of early warning systems, advancing both the forecasting capability and risk assessment of rock burst hazards up to 15 days ahead. In conclusion, this study offers a novel and rigorous methodology that not only pushes the boundaries of prediction timelines but also contributes to the advancement of safety management and monitoring systems in coal mining operations, enhancing the accuracy and reliability of rock burst early warnings.  
M.W.: conceptualization, methodology, and writing--review and editing. Z.D.: methodology, and writing--original draft. C.C.: methodology, resources, and supervision. E.W.: methodology and validation. H.J.: methodology and validation. X.L.: methodology and validation. J.W.: methodology and validation. G.S.: methodology and validation. Y.L.: methodology and validation. All authors have read and agreed to the published version of the manuscript.  
The National Natural Science Foundation of China (52227901 and 51934007); and The National Key Research and Development Program of China (2022 YFC3004705).  
Not applicable.  
The research data underlying the conclusions of this investigation are accessible through the Buertai Coal Mine Data Cloud Platform. Access to these datasets is subject to specific restrictions, as they were utilized under authorized licensing agreements for the current research. Interested parties may request access to the data from the corresponding authors, pending approval from the Buertai Coal Mine Data Cloud Platform administration.  
The authors gratefully acknowledge the financial support of the above-mentioned agencies. In addition, the authors would like to express their special thanks to the journal editors and reviewers for their hard work.  
Authors [PERSON], [PERSON] and [PERSON] were employed by the company Shanghai Datum Energy Resources Co., Ltd. Author [PERSON] was employed by the Information Institution of Ministry of Emergency Management. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.