## 1 Introduction  
As one of the key technologies of real-time traffic signal control, traffic assignment, route guidance, and other functions in the intelligent transportation system, short-term traffic flow prediction has always been the research focus. Its forecasting accuracy plays a decisive role in improving the performance of the intelligent transportation system [1]. For pursuing higher accuracy, a variety of spatio-temporal forecasting methods have been developed [2; 3]. Among them, the temporal forecasting methods are widely used and have attracted more and more attention in the recent decades. Generally, these methods are roughly divided into three categories, i.e., statistical theoretical models, intelligent models, and hybrid models. Statistical methods mainly include time-series models (e.g., autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), etc.) [4; 5; 6], Kalman filtering model [7; 8], and history average model [9]. Among them, the time series model has been widely applied in the prediction of traffic volume data. For example, [PERSON] et al. [4] developed a SARIMA model to predict the traffic flow, in which the order of model was determined by autocorrelation function and partial autocorrelation function. The forecasting results showed that the proposed model had satisfactory forecasting accuracy. [PERSON] et al. [5] proposed a short-term traffic flow forecasting model combined the ARIMA model and the space-time characteristics of the expressway network to improve forecasting accuracy. [PERSON] et al. [6] adopted an ARIMA model to forecast the traffic time-series data, and the satisfactory results could be obtained. Generally, the statistical theoretical models are simple, convenient and easy to apply. However, those models usually overlook the interferences of random factor, strong non-stationarity and nonlinearity hidden in the traffic data. Unlike the statistical methods, the intelligent models usually perform better in explaining the nonlinear relationship between the input and output. These models include artificial neural network (ANN) [10; 11; 12; 13; 14], support vector machine (SVM) [15; 16] and least square SVM (LSSVM) [17]. [PERSON] et al. [16] proposed a brand-new model integrated the wavelet function and the SVM model to forecast the target data, which could improve the forecasting results. [PERSON] et al. [18] presented a hybrid optimization algorithm combined particle swarm optimization (PSO) and genetic algorithm to find the optimal parameters of LSSVM, which could effectively improve the model's accuracy and convergence speed. [PERSON] et al. [19] introduced the proportion coefficient to combine the advantages of Gaussian kernel function and polynomial function. The forecasting result showed the built model was effective and practicable. Obviously, these intelligent models do not contain some special model architectures and have highly adaptable, especially for the nonlinear data. However, they may suffer from the problems of slow convergence speed and over-fitting. To obtain more accurate and stable prediction, many scholars have introduced a variety of hybrid models which could combine the advantages of different models. The hybrid models can be commonly divided into four types: decomposition-based methods, weighting-based methods, parameter optimization-based methods, and error correction-based methods [20]. In recent years, the decomposition-based methods have become the research focus [21]. This kind of hybrid model could use the data processing models to address the nonlinear and non-stationary features in the data, and thus the forecasting accuracy could be enhanced. The widely used decomposition algorithms have wavelet decomposition, empirical mode decomposition (EMD), and ensemble empirical mode decomposition (EEMD), etc. Among them, wavelet decomposition [22] is a multi-scale signal analysis method to tackle non-stationary signals. However, its performance usually relies on the selection of wavelet base functions.