## 6 Conclusions  
This paper used OLR data to predict 30-day values before the Yangbi _M_s6.4-magnitude earthquake on 21 May 2021 and the Luding _M_s6.8-magnitude earthquake on 5 September 2022 based on a time series prediction model and the sliding time window method and assessed the anomalies using 95% confidence intervals. Five time series prediction models were used to calculate the RMSE for 5, 10, 15, 20, 25, and 30 days in 25 grids using the Yangbi earthquake as an evaluation example. The total RMSE of the BILSTM model for the 5-day prediction time was 619.2920, which was smaller than any other combination of model and number of days. The following conclusions can be drawn:  
(1) The anomaly distributions of the Yangbi and Luding earthquakes were large. There was a correspondence between the anomaly distribution area and the distribution of fault zones. The magnitude of the anomalies of the two earthquakes was significant, and both earthquakes had showed anomaly values above 40. The anomalies of the two earthquakes showed different characteristics in the time dimension. The Yangbi earthquake anomaly appeared 15 to 16 days before the earthquake (5-6 May) with a higher intensity and more concentrated temporal distribution. The Luding earthquake showed stronger anomalies 27 (9 August), 18 (18 August), and 8 (28 August) days before the earthquake. The temporal distribution was more scattered. Overall, OLR data based on time series could effectively detect apparent pre-earthquake anomalies and analyze the possibility of earthquake generation by the characteristics presented by the anomalies, which was a promising method for earthquake prediction. (2) Based on the time series prediction model to study the seismic anomalies in the \(5^{\circ}\times 5^{\circ}\) area and within one pre-earthquake time period, we found that the Yangbi and Luding earthquakes have specific anomaly characteristics and show similar but not identical anomaly distributions. However, we may find anomalies with the same characteristics in different cases by studying many earthquake cases. This experimental anomaly extraction method used a 95% confidence interval, and there were many small and heterogeneous distribution characteristics in the results of the studied earthquake anomalies. These anomalies were minor in degree and in terms of indicative features, which may affect the interpretation of the overall seismic anomaly and need to be eliminated. Overall, the study of multiple cases based on time series prediction models is a direction for future anomaly research. The best confidence interval setting must be found in numerous cases and studies to summarize the potential patterns between the characteristics of the OLR anomaly distribution and earthquake occurrence. Conceptualization, [PERSON] ([PERSON]) and K.S. ; methodology, [PERSON] ([PERSON]); software, [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]), [PERSON] ([PERSON]), and K.S. ; formal analysis, [PERSON] ([PERSON]); investigation, [PERSON] ([PERSON]); resources, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]); visualization, [PERSON] ([PERSON]); supervision, K.S. ; project administration, K.S. ; funding acquisition, K.S. All authors have read and agreed to the published version of the manuscript. This work was funded by the National Natural Science Foundation of China under Grant No. U2039202, and the National Key Research and Development Program of China under Grant No. 2019 YFC1509202. Not applicable. This study uses NOAA-18 satellite \(1^{\circ}\times 1^{\circ}\) OLR data that can be downloaded via NCEP's FT server ([[ftp://ftp.cpc.ncep.noaa.gov/precip/noaa18_1x1/](ftp://ftp.cpc.ncep.noaa.gov/precip/noaa18_1x1/)]([ftp://ftp.cpc.ncep.noaa.gov/precip/noaa18_1x1/](ftp://ftp.cpc.ncep.noaa.gov/precip/noaa18_1x1/))) (accessed on 20 December 2022). We thank the National Natural Science Foundation of China and the National Key Research and Development Program of China for funding this study.