# A Short-Term Wind Speed Forecasting Model Based on EMD/CEEMD and ARIMA-SVM Algorithms
## 4 Experiments and Results Analysis
### Analysis of Comparative Results  
The accuracy of each step for the hybrid model has a significant impact on the final prediction results. To illustrate the modeling process of the wind speed prediction model proposed in this paper, a detailed demonstration of the modeling process is presented in this section, taking dataset 1 as an example. Firstly, the signal decomposition algorithms of EMD and CEEMD are introduced to decompose the original wind speed into a series of IMFs and a residual component, as shown in Figure 3. The raw wind speed presents a large fluctuation range, while volatility of the decomposed IMF components behave much more smoothly. With the decrease in the fluctuation scales, the IMF series gradually tends to be stationary. A total of seven IMF subseries are searched by CEEMD, while EMD only determine four IMF subseries. Generally, the decomposition process may be affected by the volatility level of the wind speed series, to some extent. More IMFs can be obtained for wind datasets with larger fluctuations and complex frequency components. Owing to the adding of opposing white noise to the original wind speed, the CEEMD effectively avoids the disadvantages of modal mixing of EMD, which is a more ongoing decomposition method. As the CEEMD algorithm has a higher decomposition precision, it is easy for the CEEMD algorithm to determine more IMF components with different scales. Figure 3: EMD and CEEMD decomposition diagram of dataset 1. Then, the ARIMA model is adopted to rebuild the IMF subseries, and the forecasting wind speed subseries are available, taking advantage of the established ARIMA model. The prediction results are depicted in Figure 4. The model presents good prediction effects for the low-frequency IMF components, as the ARIMA has a strong capturability of the volatility of the wind speed subseries with low-frequency. It shows that ARIMA models have sufficient prediction accuracy for low-frequency components. However, a certain prediction error exists for the high-frequency components. However, due to the linear features of ARIMA, the prediction model is inevitably subject to prediction error for the forecasting of the high-frequency components. Further, the prediction errors of all components are superimposed to reconstruct a new error subseries. Given that nonlinear features of the error subseries, the SVM are employed to predict the error results, as shown in Figure 5. In spite of the complex volatility of the error subseries, the SVM model fits the prediction value close to the true value, to some extent, which displays the great power of the nonlinear processing ability. It is feasible to apply the SVM model to forecast the error subseries. Finally, the prediction results are obtained by superimposing all of the prediction values gained from the above steps. To further verify the stability and accuracy of the model proposed in this paper, a comparative analysis is conducted between the model and five other representative prediction modes, namely ARIMA, SVM, ARIMA-SVM, EMD-ARIMA, and CEEMD-ARIMA. The comparative results are depicted in Figures 6-8, which show the deviations between the raw wind speeds and the forecasted wind speeds according to the prediction models. The proposed models show good performances; the prediction values are much closer to the true wind speed than other prediction models. Table 2 lists the quantitative evaluation index results using the prediction models for the three datasets. Figure 4: ARIMA prediction results. Figure 5: SVM prediction results. Figure 8: Comparative prediction results for dataset 3. Figure 6: Comparative prediction results for dataset 1. Figure 7: Comparative prediction results for dataset 2. It is clearly seen from the figures and Table 2 that the evaluation indexes for the single ARIMA and SVM models are much more accurate than those for the other hybrid models, which indicates the single ARIMA and SVM prediction models have low accuracy. Moreover, the prediction wind speed presents the phenomenon of hysteresis.