## 4 Performance Comparison of Different Models
### Stationary Test and Model Building
Figure 5: Differenced dataset. (**a**) Data obtained after first-order difference; (**b**) PDF of differenced dataset. Figure 6: ACF and PACF of the differenced dataset. (**a**) ACF; (**b**) PACF. indicates that the residual errors are Gaussian with a zero mean and standard deviation of 0.0141. The ACF suggests that there is no significant correlation between the errors and its own lagged values. Based on these observations, the established ARIMA(6,1,2) model provides a satisfied estimation of the training set. For the training of BPNN, there are two main hyperparameters determining the topology, which are the number of layers and the number of neurons in each layer. The first and last layer are generally identified as the input layer and the output layer, whose functions are accessing data and providing results, respectively. The nodes of the input layer equal to the number of focused elements. In this study, the wind speed is the only dependent variable to conduct forecasting. The result of PACF presented in Figure 6 suggests that the wind speed is related with the previous 6 min historical data. The number of nodes in the input layer is set as 6 in BPNN. When it comes to nodes in the output layer, the number depends on the steps ahead for prediction. For example, six nodes will be employed for a six-step ahead forecasting model. Although there is no unified standard to determine the number of hidden layers and the quantity of nodes in each hidden layer, studies suggest that a single hidden hidden layer is generally competent in the large majority of engineering problems, and this configuration is utilized for BPNN in this study [22; 46; 47; 48]. Moreover, these studies also suggest that the optimal size of the hidden layer is usually taken between those of the input and output layers. Based on several training and associated comparisons, six nodes are employed for the single hidden layer of BPNN in this study. With respect to the training of the LSTM network, a more accurate prediction can be obtained with more memory cells. To facilitate a comparison with the BPNN model, the LSTM network employs one hidden layer and six memory cells within the hidden layer as well. As the memory cell of LSTM can automatically access data and reserve the previous information, there is no need for an additional input layer, and the configuration of the output layer is similar to that of BPNN. For the network with GRU cells, the structure is the same as that with the LSTM cell. The only difference is that each neuron is represented via the GRU cell instead of the LSTM cell. On the SVR model based on the radial basis function kernel, the dimension of the input feature vector is equal to 6 according to the PACF of observed data. Then, the undetermined parameters can be estimated using the least-square method. When it comes to the multi-step ahead forecasting, different SVR models need to be developed as the number of output variates varies. Given the discussions above, there are five models included for the subsequent short-term wind speed forecasting. The models and their corresponding descriptions are given in Table 2. Note that the assigned parameters of each model vary for different tropical storms. Figure 7: Analysis of residual components. (**a**) PDF; (**b**) ACF. As the wind speeds present typical time-varying features, a dynamic training strategy that updates the model coefficients when new observations are obtained is more appealing. However, this strategy is only suitable for ARIMA and SVR models because the training of artificial neural network-based models is time-consuming, which is contradictory to the purpose of early warning. Moreover, the optimization in training artificial neural networks has salient uncertainties in the trained results. Specifically, the error of the training set decreases with training epochs, while the error of the validation set firstly decreases with the training epochs before the optimum epoch and increases afterwards.