# A Short-Term Wind Speed Forecasting Model Based on EMD/CEEMD and ARIMA-SVM Algorithms
## 1 Introduction  
Gales are one of the main causes of ground transportation meteorological disasters, especially in the windy areas of southeast coastal China. On typhoon days, severe convective weather, accompanied by strong winds and heavy rains, seriously threatens the driving safety of high-speed trains and road vehicles. In order to ensure the driving safety of vehicles, the wind monitoring and warning system [1] is becoming one of the major mitigation measures for wind-induced transportation accidents. As the monitoring value, or the forecasted wind speed, exceed the alarm threshold, the early-warning signal is issued to alert the running vehicles, avoiding wind-induced accidents [2, 3]. Thereby a highly accurate short-time wind speed forecasting model is particular important to ensuring reliable operation of the system. In general, wind speed prediction methods can be roughly divided into two categories according to the prediction principle [4]: (1) physical meteorological models based on atmospheric dynamic equations; (2) statistical methods based on historical observation data. The statistical methods are constructed based on vast historical observations of windspeed. These are particularly suitable for short-term wind speed forecasting. Conventional statistical models, such as autoregressive models [5], neural network models [6; 7], Kalman filters [8], and support vector machines [9] are currently widely used. However, the wind has the characteristics of randomness, periodicity, non-stationarity, and nonlinearity. In order to further improve the accuracy and stability of the wind speed prediction algorithm, the hybrid methods, combining the complimentary features and advantages of various methods, have been favored by scholars. The hybrid wind speed forecasting methods based on decomposition techniques are developing rapidly. Techniques such as EMD (Empirical Mode Decomposition) and WPT (Wavelet Packet Transform) [10], are used as data preprocessors to decompose wind series or eliminate stochastic volatility. The fast ensemble EMD is adopted by [PERSON] et al. [11] to decompose the original wind speed series into a number of sub-layers, and the MLP (Multi-Layer Perceptron) neural networks optimized by MEA (Mind Evolutionary Algorithm) and GA (Genetic Algorithm) are built to predict the decomposed wind speed sub-layers. Two hybrid methods are proposed for the accurate multi-step wind speed prediction. [PERSON] et al. [12] develop an EMD-based decomposition selection forecasting (DSF) model for wind speed prediction. The DSF model is achieved using artificial neural networks (ANNs) and support vector machines (SVM), which is verified to be effective with great precision. [PERSON] et al. [13] propose a hybrid forecasting approach that combines the extreme learning machine, the Ljung-Box Q-test, and the seasonal ARIMA to enhance the accuracy of wind speed forecasting; the results show that the developed hybrid method exhibits stronger forecasting ability. [PERSON] et al. [14] investigate the prediction performances of the hybrid model combining ARIMA and ANN. [PERSON] et al. [15] construct a hybrid wind speed prediction model integrating ensemble EMD and an adaptive wavelet neural network (AWNN), which has high accuracy, strong stability, and advantages of a small amount of calculation. A big hybrid WPD-Boost-ENN-WPF framework for multi-step wind speed prediction, consisting of wavelet packet decomposition (WPD), Elman neural network (ENN), boosting algorithms, and wavelet packet filter (WPF) are proposed [16]. On the basis of a hybrid model decomposition method (HMD) and online sequence outlier robust extreme learning machine (OSORELM), a hybrid short-term wind speed prediction model is developed by [PERSON] et al. [17]. The results show that the online model and deep decomposition technology improve the stability and accuracy of model prediction. Considering the correlation of the prediction errors, [PERSON] et al.