To improve the prediction effect of time series, we make a systematic study of various time series prediction methods based on statistics and machine learning in this paper. In the experiment, we compare the prediction results of several prediction methods. In particular, much research has been done on the selection of experimental data because representative time series data can better test the effectiveness and practicability of the prediction method. Based on the idea of divide and conquer of complex problems and the strategy of continuous optimization of machine learning, we proposed the prediction methods of LSTM-TFE, LR-TFE, and BR-TFE combined the EEMD, LSTM, LR, and BR methods in this paper. These methods use EEMD to decompose complex time series into several relatively milder, more regular and stable subsequences. Then the prediction model of each subsequence based on machine learning is carried out by using the LSTM, LR, or BR methods. We use these prediction models to predict the value of each subsequence. Finally, the value of multiple subsequences is fused to form the prediction results of the original complex time series. To verify the proposed method comprehensively, we select three representative time series data to test this paper. From the experimental results, we found that the proposed method has a good effect.

The stock market is a chaotic, complex, and dynamic financial market. The prediction of future stock prices is a concern and controversial research issue for researchers. More and more analysis and prediction methods are proposed by researchers. We proposed a hybrid method for the prediction of future stock prices using LSTM and ensemble EMD in this paper. We use comprehensive EMD to decompose the complex original stock price time series into several subsequences which are smoother, more regular and stable than the original time series. Then, we use the LSTM method to train and predict each subsequence. Finally, we obtained the prediction values of the original stock price time series by fused the prediction values of several subsequences. In the experiment, we selected five data to fully test the performance of the method. The comparison results with the other four prediction methods show that the predicted values show higher accuracy. The hybrid prediction method we proposed is effective and accurate in future stock price prediction. Hence, the hybrid prediction method has practical application and reference value.

To effectively improve the power dispatching, the prediction accuracy of wind power has been the concern of many scholars for many years. The wind power prediction problem is actually equivalent to the wind speed prediction problem. Based on linear regression (LR) and variational mode decomposition (VMD), in this paper, we proposed an efficient hybrid method to predict wind speed. In the proposed method, the VMD is used to decompose the signal of wind speed into several sub-signal. Compared with the original wind-speed series, each sub-signal is a more stable subsequence signal. Then, we used the LR method to predict each subsequence signal. Eventually, we obtain the final prediction results of the original wind speed series merged the forecasting values of all subsequences signal. We selected two data to test our proposed method in our experiment. Compared with several comparison methods, we found that our proposed methods has better prediction performance than other methods from the experimental results.

This study, based on engineering education accreditation standards, addresses the supply–demand imbalance in China’s cybersecurity talent cultivation by constructing a sustainable “education-industry-society” collaborative model. Through case studies at Huaihua University and other institutions, employing methods such as literature analysis, field research, and empirical investigation, we systematically explore reform pathways for an innovative cybersecurity talent development system. The research proposes a “three-platform, four-module” practical teaching framework, where the coordinated operation of the basic skills training platform, comprehensive ability development platform, and innovation enhancement platform significantly improves students’ engineering competencies (practical courses account for 41.6% of the curriculum). Findings demonstrate that eight industry-academia practice bases established through deep collaboration effectively align teaching content with industry needs, substantially enhancing students’ innovative and practical abilities (172 national awards, 649 provincial awards). Additionally, the multi-dimensional evaluation mechanism developed in this study enables a comprehensive assessment of students’ professional skills, practical capabilities, and innovative thinking. These reforms have increased the employment rate of cybersecurity graduates to over 90%, providing a replicable solution to China’s talent shortage. The research outcomes offer valuable insights for discipline development under engineering education accreditation and contribute to implementing sustainable development concepts in higher education.