## 4 Conclusions  
In this paper, the production safety accidents of housing municipal engineering are analyzed, and the time series under these three granular sizes were predicted, respectively, because the long-term trends of the annual, quarterly, and monthly data of the number of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Combination**} & \multirow{2}{*}{**Item**} & \multicolumn{2}{c}{**Indicator**} & \multicolumn{1}{c}{**Indicator**} & \multicolumn{1}{c}{**Amount of**} & \multirow{2}{*}{**Weight**} \\  & & & **Variability** & **Conflict** & **Information** & \\ \hline A & M5 & 0.339 & 4.229 & 1.435 & 24.12\% \\ \hline B & M4 & 0.37 & 5.084 & 1.881 & 26.48\% \\ \hline C & M5 & 0.329 & 4.65 & 1.53 & 24.98\% \\ \hline D & M5 & 0.347 & 4.825 & 1.676 & 25.12\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of the maximum specific gravity for each combination of CRITIC weights. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Trend**} & \multirow{2}{*}{**Seasonal**} & \multirow{2}{*}{**Residual**} & \begin{tabular}{c} **Corrected** \\ **Value** \\ \end{tabular} & \begin{tabular}{c} **Corrected** \\ **Interval** \\ \end{tabular} & \begin{tabular}{c} **Predictive** \\ **Interval** \\ \end{tabular} & \
\begin{tabular}{c} **Raw** \\ **Value** \\ \end{tabular} &
\begin{tabular}{c} **Relative** \\ **Error\%** \\ \end{tabular} \\ \hline
2019 & 914 & 0 & \(-13\) & 70 & \([-83,57]\) & \([831,971]\) & 904 & \([-8.05,7.41]\) \\ \hline
2020 & 1007 & 0 & 23 & 68 & \([-45,91]\) & \([952,1098]\) & — & — \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Predictive results of the death toll range in housing municipal engineering production safety accidents. accidents and deaths remained highly correlated. Predicting the same research object will lead to different prediction accuracy due to different data granularity, and the combined model correction will be carried out for the time series under different circumstances, so as to continuously improve the prediction accuracy. Finally, the conclusions are as follows:  
(1) Using monthly data and quarterly data of accidents from 2009 to 2019 for training through the ARMA model, which compares dynamic prediction and static forecast results in 2019, and further determines the optimal parameters of the ARIMA model. Among them, the monthly forecast should also consider the impact of the Spring Festival, and a fractional grey accumulation model based on genetic algorithms is constructed to predict accidents in January and February alone. The relative error of accident prediction in 2019 is reduced from 3.23% to 2.33%. Decompose the time series and predict the trend components and remainder quantities separately. The predictive value obtained after the addition of the monthly and quarterly accident prediction results and the Pearson correlation coefficient of the raw value exceeds 0.9, indicating a strong correlation. The relative error of the forecast is reduced to 0.39% by using the quarterly data, which improves the prediction accuracy. It confirms the effectiveness of the model in monthly and quarterly prediction, effectively reflecting the development trend of accidents. And for monthly, people should focus on situations with large changes and strengthen safety management during the period. For quarterly, it is necessary to analyze the accident situation of the current quarter in a timely manner and summarize the lessons at the end of each quarter. (2) When forecasting the number of annual accidents, due to the small amount of sample data and the absence of regularity, the ARIMA model cannot be used for prediction. Based on this, the rolling forecast is carried out which uses a two-yearly forecast, three-yearly forecast, and four-yearly forecast from 2009. The final forecast value for 2009-2019 is revised by calculating the proportion of the forecast results under different forecast paths.