Chong Liu

The fractional-order grey prediction model is widely recognized for its performance in time series prediction tasks with small sample characteristics. However, its parameter-estimation method, namely the least squares method, limits the predictive performance of the model and requires time to address the ill-conditioning of the system. To address these issues, this paper proposes a novel parameter-acquisition method treating structural parameters as hyperparameters, obtained through the marine predators optimization algorithm. The experimental analysis on three datasets validate the effectiveness of the method proposed in this paper.

Accurately predicting quarterly or monthly energy consumption remains challenging so far. Despite the abundance of relevant studies, most of them focus on univariate modeling. Moreover, the core of nearly all multivariate forecasting studies is an unstable forecasting system based on a single model. Therefore, there is an urgent need for an efficient and rational prediction method. For the prediction task of quarterly or monthly energy consumption characterized by small samples and nonlinearity, this article develops a new joint forecasting-centered forecasting framework by integrating machine learning and grey system theory. In this forecasting framework, grey relational analysis is used to filter the influencing factors of the study object, a new adaptive weighted least squares support vector regression model is developed to describe the relationship between the study object and the filtered influencing factors, and a new difference equation prediction model is employed to predict the future values of the filtered influencing factors. The joint forecasting task is accomplished by inputting the future values of the filtered influencing factors into the trained adaptive weighted least squares support vector regression model. Experimental simulation results demonstrate that the two prediction models developed in this framework, along with the overall forecasting approach, outperform competing methods. These results confirm the effectiveness of the proposed forecasting framework in accurately predicting quarterly or monthly energy consumption, even in scenarios with limited data and nonlinear relationships.

Against the backdrop of Sino-US trade friction, we use Chinese President Xi Jinping’s visit to rare earth companies as an exogenous event to investigate market investors’ pursuit effect, on 11 rare earth companies. We find that (1) Xi’s visit had an important signal effect, causing abnormal fluctuations in the stock prices and a significantly positive cumulative average abnormal return (CAAR) of the 11 companies . (2) After government political visits (GPV), firms located in developing regions, younger firms, firms with poor historical performance, and firms with high ownership concentration can generate higher cumulative excess returns than those in developed regions, more established firms, firms with better financial performance, and firms with lower ownership concentration. (3) Our findings can be interpreted from two perspectives: the information asymmetry between market investors and enterprises and the signal effects of GPV. Finally, we discuss policy recommendations from the perspective of information transfer and securities analysts.

This paper investigates the novel control scheme of robust nonlinear disturbance observer (RNDO) with backstepping technique for the tracking of the underactuated marine surface vehicle (USV). In order to enhance systems’ robustness, RNDO is designed to estimates external environment disturbance in finite-time. Furthermore, the finite time tracking controller is developed via nonsmooth backstepping technique incorporating the RNDO to achieve position tracking , yaw tracking and velocity tracking control. In order to solve the problem of "complexity explosion" in the traditional backstepping method, a nonlinear filter is introduced to generate the derivative of virtual control law. A Lyapunov-based stability analysis proves that the tracking error of USV can converge into a small neighborhood around zero in finite time.

As an essential characteristics of fractional calculus, the memory effect is served as one of key factors to deal with diverse practical issues, thus has been received extensive attention since it was born. By combining the fractional derivative with memory effects and grey modeling theory, this paper aims to construct an unified framework for the commonly-used fractional grey models already in place. In particular, by taking different kernel and normalization functions, this framework can deduce some other new fractional grey models. To further improve the prediction performance, the four popular intelligent algorithms are employed to determine the emerging coefficients for the UFGM(1,1) model. Two published cases are then utilized to verify the validity of the UFGM(1,1) model and explore the effects of fractional accumulation order and initial value on the prediction accuracy, respectively. Finally, this model is also applied to dealing with two real examples so as to further explain its efficacy and equally show how to use the unified framework in practical applications.