## 1 Introduction
Benefits included being able to handle any type of data, allowing components to change over time at a variable rate, and being insensitive to outliers. For decomposed time series, the selection of a recombination method is also very important. [PERSON] [9] used wavelet transform to decompose and recombine the time series to obtain prediction results of the fuzzy range of mining safety production accidents, which also helped to reduce randomness and uncertainty in the forecast. This demonstrated the effectiveness of model building and incorporating the original data decomposition method. In addition, as one of the most common models for accident prediction, the grey prediction method can solve the problems of low historical data and low sequence integrity, but its ability to process long-term data is weak and only reflects a single change law. To this end, [PERSON] [10] comprehensively considered influencing factors such as the number of construction enterprises and the number of employees and added them to the development process of the model. It was proved that improving the on-site management system of construction enterprises and improving the management level can reduce the accidents, which are the key points in controlling the number of deaths in construction safety accidents. By preprocessing and analyzing the time series in a single prediction model and using artificial intelligence algorithm optimization, data volatility can be reduced, and prediction accuracy can be improved. The optimized single prediction model is still not systematic and comprehensive. However, the combined prediction model can maximize the use of single prediction results, avoid the lack of effective information, and reduce the impact of accidental factors. For now, research to improve the accuracy and stability of predictions is still focused on weakening the volatility of the series. For example, [PERSON] et al. [11], based on the influence of variables, introduced seasonal effects into short-term power load forecasting. Variables were effectively integrated into the prediction model of the combination of the firefly algorithm and support vector machine, which proved that the addition of other factors makes the prediction more realistic than a single one. In terms of eliminating the difficulties caused by non-stationary sequences, [PERSON] et al. [12] used different modelsto predict signal sequences of different frequencies, demonstrating that the EMD-GM-ARMA combination model can accurately predict short-term and medium-term mine safety production conditions. [PERSON] [13] used the X-12-ARIMA model combined with seasonal factors to predict the evolution trend of mine production safety conditions so that the prediction results were in line with the actual trend of coal mine accidents. To make the fluctuation trend more realistic, [PERSON] [14] used the times series Markov model and considered the impact of the recent situation, and the final prediction error value of the TSM model, the precision of which was affirmed to be significantly smaller than that of the TS model. In general, the above research has mainly focused on the analysis of a certain factor in a specific field in a long-term time range. They optimized and combined the classical prediction model to form an accident prediction model suitable for this field. Among them, the ARIMA model only needed endogenous variables and did not need to rely on other exogenous variables (relying only on the data itself, unlike regression that requires other variables) and had a good effect on non-stationary time series. However, it cannot capture nonlinear relationships. Grey forecast is a method of accumulating (or other processing to generate) the original data to obtain an approximate exponential law and then modeling, which can be applied to situations with less historical data and low sequence integrity and reliability. However, their long-term forecasts were weak and unable to handle volatile data [15].