## 3 Results and Discussions
### Accident Number Prediction  
#### 3.2.1 Monthly Forecast  
The number of accidents in January and February is at a low value throughout the year, because the Spring Festival in China is mainly concentrated in January and February, and the holiday effect increases the overall fluctuation of the time series. Due to the uncertainty of the Spring Festival time and the generally small number of accidents in January and February, the two months are extracted for separate forecasts, and the specific impact range distribution of the Spring Festival in each year is no longer considered. The optimal  
Figure 4: Autocorrelation plots and partial autocorrelation plots. Figure 5: Monthly forecast results of housing municipal engineering production safety accidents in 2019.  
combination of ARMA (10, 10) is found in the same way as the data training, and it is found that the relative errors of the two predictions in the remaining 10 months of 2019 are 4.6% and 15.9%, the correlation coefficients with the true values are 0.824 and 0.862, and the fitting degree was 0.738 and 0.591, although the degree of fit and related coefficients decreased, to a certain extent, overfitting was avoided. Using the above optimal combination, the ARIMA (10, 1, 10) process is set to predict the number of accidents from March to December, and the number of accidents in January and February is predicted by using the fractional grey accumulation model. After calculation, it is found that the relative error between the predicted value and the actual value of the number of accidents in 2019 has been reduced from 3.23% to 2.33%, indicating that this method can improve the prediction accuracy. Table 3 shows the forecasts before and after the model revisions for 2019 and 2020. Repeat the above operation if you first perform trend decomposition on the original time series and then use the trend component as a new time series. It is found that the optimal prediction process in the case of trend decomposition is obtained by setting the ARIMA (4, 2, 12) process to predict the long-term trend, adding the seasonal component and the remainder predicted by the ARIMA (6, 0, 6) model. Table 4 shows the forecast results after the trend decomposition. The relative error between the predicted value and the true value of the monthly accident number in 2019 decreases from 3.23% to 2.46%. At the same time, the correlation coefficient increases from 0.861 to 0.932, and the fitting degree is 0.868, which demonstrates the forecast development trend is closer to the real situation. This shows that the model can effectively reflect the monthly development trend of accidents and guide the monthly rectification of the construction industry. To prevent accidents, relevant departments should \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Year** & **Trend** & **Seasonal** & **Residual** & **Predictive Value** & **Raw Value** & **Relative Error** \\ \hline
2019 & 777 & 0 & \(-\)23 & 754 & 773 & 2.46\% \\ \hline
2020 & 853 & 0 & 5 & 858 & — & — \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Monthly forecast and additional results of housing municipal engineering production safety accidents after trend decomposition.