# A Short-Term Wind Speed Forecasting Model Based on EMD/CEEMD and ARIMA-SVM Algorithms
## 5 Conclusions  
Improving the wind speed prediction accuracy is of great significance for the operation of the wind monitoring and warning system, which is involved in the running safety of vehicles under crosswinds. In this paper, a novel short-term wind speed prediction model, combined with EMD/CEEMD, ARIMA, and SVM, is proposed for the application of forecasting typhoon wind speed. In the proposed EMD/CEEMD-ARIMA-SVM model, the EMD or CEEMD is adopted to decompose the original wind speed into a series of subsequences. The ARIMA is used to predict the wind speed of the subsequences. Finally, the prediction errors of all of the subsequences are reconstructed and predicted by the SVM algorithm. The efficiency and accuracy of the model are verified by three wind speed sample datasets, in comparison with the others wind prediction models. The following conclusions can be drawn: \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Dataset 1**} & \multicolumn{3}{c}{**Dataset 2**} & \multicolumn{3}{c}{**Dataset 3**} \\ \cline{2-10}  & **MAE** & **MAPE** & **RMSE** & **MAE** & **MAPE** & **RMSE** & **MAE** & **MAPE** & **RMSE** \\  & **(m/s)** & **(\%)** & **(m/s)** & **(m/s)** & **(\%)** & **(m/s)** & **(m/s)** & **(\%)** & **(m/s)** \\ \hline ARIMA & 1.787 & 25.122 & 2.269 & 1.747 & 29.222 & 2.116 & 0.557 & 9.768 & 0.751 \\ SVM & 1.391 & 18.777 & 1.748 & 0.746 & 11.226 & 1.091 & 0.940 & 15.959 & 1.267 \\ ARIMA-SVM & 1.821 & 25.851 & 2.283 & 1.767 & 29.627 & 2.116 & 0.556 & 9.829 & 0.753 \\ EMD-ARIMA & 1.037 & 14.178 & 1.271 & 1.036 & 17.708 & 1.264 & 0.498 & 8.381 & 0.655 \\ CEEMD-ARIMA & 0.669 & 8.046 & 0.849 & 0.417 & 6.710 & 0.537 & 0.289 & 5.026 & 0.375 \\ EMD-ARIMA-SVM & 1.027 & 14.075 & 1.260 & 1.032 & 17.548 & 1.245 & 0.477 & 8.103 & 0.628 \\ CEEMD-ARIMA-SVM & 0.664 & 8.098 & 0.839 & 0.412 & 6.672 & 0.529 & 0.288 & 5.010 & 0.377 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation index results for the prediction models. The hybrid model shows higher prediction accuracy than the single model. The hybrid model is more suitable for higher volatility of wind speeds, exhibiting the ability to capture the fluctuating characteristics of wind speeds, while the single ARIMA model is more suitable for less volatile data. The EMD and CEEMD can reduced the nonstationarity and nonlinearity of the original wind speed. It decomposes the raw wind speeds into a series of subsequences, greatly reducing wind speed volatility. The prediction accuracy of the hybrid models has been obviously improved with the aid of the decomposition technologies, such as EMD and CEEMD, since CEEMD has removed the disadvantage of the appearance of modal mixing for EMD. Overall, the prediction performance of the CEEMD-ARIMA-SVM is better than that of the EMD-ARIMA-SVM model. Taking the three wind speed datasets as experiment examples, the prediction performance of the proposed EMD/CEEMD-ARIMA-SVM wind prediction model achieved optimum results according to the minimal evaluation indexes of MAE, MAPE, and RMSE. It seems that the prediction performance of the hybrid model mainly relies on the combination of CEEMD with ARIMA. The SVM method has only slight effects on the prediction performances of the hybrid model, as the error subseries prediction results using the SVM method show few improvement effects on the overall prediction results. In a summary, the hybrid wind speed prediction model proposed in this paper is a feasible wind speed forecasting algorithm. It has sufficient accuracy for typhoon wind speed prediction. The method can be treated as an alternative wind speed prediction method, which can also be applied to other wind speed prediction scenarios, such as the wind power prediction for wind farms, the migration of pollutants in the fields of environmental protection, and so on.