## 1 Introduction  
Cross-sea bridges that connect different regions are becoming a more and more important part of the modern transportation network. With cross-sea bridges being longer and lighter, they are more susceptible to wind load. Under strong winds, the serviceability of a long-span cross-sea bridge can be influenced, and there may even be traffic accidents [1, 2, 3]. Therefore, there is an urgent need to establish a reliable wind speed prediction model to predict wind speed near the bridge and then aid in the management of the bridge. A wind speed prediction model can be mainly classified into physics-driven models and data-driven models. Physics-driven models collect related meteorological parameters and employ numeric weather prediction (NWP) models to make wind speed predictions, which require many computing resources and are more suitable for long-term predictions. In addition, post-processing techniques are usually needed for physics-driven models to make wind speed predictions for a specific location [4, 5, 6]. Data-driven models predict wind speed by modeling evolutionary mechanisms of wind speed embedded in historical data. As a comparison, data-driven models require fewer computing resources and are more suitable for short-term wind speed prediction for a specific location. According to the aforementioned comparison, data-driven models are more suitable for short-term wind speed prediction for long-span cross-sea bridges. Traditional data-driven models are time series models, such as the autoregressive moving average (ARMA) model [7] and fractional autoregressive integrated moving average (f-ARIMA) model [8]. Time series models mainly focus on linear characteristics in the historical wind speed data. In order to take nonlinear characteristics into consideration more efficiently, machine learning models such as support vector machine [9], multi-layer perceptron [10] and extreme leaning machine [11] were adopted. With the increase in the volume of data and the development of data mining techniques, deep learning models were also applied to predicting wind speed, which can extract multi-level features more efficiently than shallow models. Typical examples are the long short-term memory (LSTM) neural network [12], convolutional neural network (CNN) [13] and deep reinforcement learning model [14]. The accuracy of a data-driven model is easily influenced by the unpredictable noise contained in the historical data. To alleviate the degradation of accuracy caused by the noise, researchers proposed to combine the data decomposition technique with the prediction model. The basic idea is as follows. The raw wind speed series is firstly decomposed into several subseries that are easier to predict. Then, the prediction model is established for each subseries, and the final prediction is made by aggregating the predictions for each subseries. For example, [PERSON] et al. [15] proposed a prediction model with empirical mode decomposition (EMD) and ARIMA. [PERSON] et al. [16] came up with a prediction model with wavelet packet decomposition (WPD) and the Elman neural network (ENN). [PERSON] et al. [17] used singular spectrum analysis (SSA) to decompose the wind speed data and adopted a convolutional neural network gated recurrent unit (CNNGRU) and support vector regression (SVR) to predict the trend and detail components, respectively. Other signal processing techniques such as wavelet transform (WT) [18] and variational mode decomposition (VMD) [19] were also employed. To further enhance the predictability of a subseries, a secondary decomposition is needed. [PERSON] et al. [20] firstly used complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the raw series and then used empirical wavelet transform (EWT) to decompose a subseries of high frequency. [PERSON] et al. [21] employed VMD to decompose the raw series and then applied WT to denoise each subseries.