Stock price prediction is an important and complex time-series problem in academia and financial industries. Stock market prices are voted by all kinds of investors and are influenced by various factors. According to the literature studies, such as Elliott’s wave theory and Howard’s market cycle investment theory, the cyclic patterns are significant characteristics of the stock market. However, even several studies that do consider cyclic patterns (or similar concepts) suffered from the data leakage or boundary problems, which could be impractical for real applications. Inspired by the abovementioned, we propose a hybrid deep learning model called mWDN-LSTM, which correctly utilizes the cyclic patterns’ information to predict stock price while avoiding the data leakage and alleviating boundary problems. According to the experiments on two different datasets, our model mWDN-LSTM outperforms the well-known benchmarks such as CNN-LSTM on the same experimental setup and demonstrates the effectiveness of utilizing cyclic patterns in stock price prediction.

H. R. Wen

Mingchuan Yuan

Shuxin Wang

Lixin Liang

Xianghua Fu

A Multilevel Wavelet Decomposition Network Hybrid Model Utilizing Cyclic Patterns for Stock Price Prediction

This article investigates the decentralized formation tracking problem for complex multiagent systems in finite settling time, subjected to output constraints and external disturbances. The barrier Lyapunov function is used to constrain the output of each agent. Therefore, starting inside a closed boundary, the agents are guaranteed to remain inside it for all future time. Under directed communication topology, decentralized time-varying formation tracking is achieved in finite time. Furthermore, in the proposed work, the linear sliding manifold is employed to mitigate the singularity problem that occurs in the conventional robust finite-time methods, i.e., terminal sliding mode-based control schemes. The stability properties of the proposed framework are established through the Lyapunov method which not only ensures the finite-time formation tracking of nonlinear multiagent systems under directed communication but also guarantees that for all time the agents remain inside a closed boundary if they are initially inside it. Consequently, the uniqueness of this article is that it presents a novel formation tracking control framework for multiagent systems that simultaneously considers three performance metrics of robustness, finite-time convergence, and output constraints while mitigating the singularity problem. The proposed topology is validated by implementing the numerical examples in MATLAB/SIMULINK.

Naveed Mazhar

Rameez Khan

Abid Raza

Fahad Mumtaz Malik

Raja Amer Azim

Hameed Ullah

Robust Decentralized Formation Tracking Control of Complex Multiagent Systems

To meet the personalized distribution needs of customers, comprehensively consider customer value, the urgency of customer needs, and the impact of priority distribution to the customer on the enterprise, and based on regional restrictions, put forward vehicle-drone joint distribution path optimization problem considering customer priority. First, the goal is to minimize the sum of total distribution cost and customer priority cost integrating soft time windows and constructing a path optimization model of the vehicle-drone joint distribution. Second, a two-stage hybrid algorithm is proposed for the problem model. In the first stage, the deep neural network and the grid search improved support vector machine algorithm (DNN-GSM-SVM) are used to screen and classify customer priority features, and in the second stage, adaptive large-scale neighborhood search improved genetic algorithms (ALNS-GA) are used to solve the problem of vehicle-drone joint distribution path planning problem. Finally, combined with the numerical example, the optimization scheme of the vehicle-drone joint distribution path considering priority is analyzed. Compared with the three algorithms and error analysis, the effectiveness of the model and the two-stage algorithm was verified. Compared with the results of the scheme that does not consider priority, the results show that priority can significantly improve customer satisfaction. The efficiency of the vehicle-drone joint distribution was verified by comparing the three scenarios.

Xiaoye Zhou

Yuhao Feng

Research on Path Optimization of Vehicle-Drone Joint Distribution considering Customer Priority

Stock ranking prediction is an effective method for achieving a high investment return and plays a crucial role in investment decisions. However, previous studies have overlooked the interconnections among stocks or have solely relied on predefined graphs for stock relationship information. The predefined graphs may not capture all possible relationships and may not be suitable for describing dynamic relationships. To address these issues, we propose a Static-Dynamic hypergraph neural network framework based on Residual Learning (SD-RL). Compared with traditional methods, SD-RL has the following advantages. (1) Stocks are no longer treated as isolated entities; instead, their static and dynamic relationship information is taken into account. (2) Leveraging the data-driven methodology, SD-RL autonomously learns both the static graph and dynamic hypergraph through dedicated graph learning and hypergraph learning modules, respectively. (3) By employing residual learning, various latent relationship information flows are mined, which enhances the stock embedding’s capacity to capture trends. Extensive experiments on the real data verify the effectiveness of our proposed methods.

Jianlong Hao

Zhibin Liu

Qiwei Sun

Chen Zhang

Jie Wang

A Static-Dynamic Hypergraph Neural Network Framework Based on Residual Learning for Stock Recommendation

The item and pod storage assignment problems, two critical issues at the strategic level in robotic mobile fulfillment systems, have a strong correlation and should be studied together. Moreover, the workload balance in each picking aisle needs to be considered in the storage assignment problems to avoid robots’ congestion within picking aisles. Motivated by these, the joint optimization of item and pod storage assignment problems (J-IPSAP) with picking aisles’ workload balance is studied. The mixed integer programming model of the J-IPSAP with the workload balance constraint is formulated to minimize the robots’ movement distance. The improved genetic algorithm (IGA) with the decentralized pod storage assignment strategy is designed to solve the J-IPSAP model. The experimental results show that the IGA can obtain high-quality solutions when compared with Gurobi and the two-stage heuristic algorithms. The robots’ movement distance is smallest when the width-to-length ratio of the storage area is close to 1, and the robots’ movement distance will increase with more stringent workload balance constraints.

Jun Zhang

Lingkun Tian

Zijuan Zhou

Joint Optimization of Item and Pod Storage Assignment Problems with Picking Aisles’ Workload Balance in Robotic Mobile Fulfillment Systems

The problem of boundary values for implicit differential equations with nonlinear fractions involving the variable order and the Riemann–Liouville derivative is examined in this article along with its existence and stability. Specifically, the locally solvability, which is equivalent to the existence of solutions, is related to the symmetry of a transformation of a nonlinear equations system. To demonstrate the reliability of the found results, we design an example.

Zoubida Bouazza

Mohammed Said Souid

Hatıra Günerhan

Ali Shokri

Problem Involving the Riemann–Liouville Derivative and Implicit Variable-Order Nonlinear Fractional Differential Equations

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Complexity publishes original research and review articles across a broad range of disciplines with the purpose of reporting important advances in the scientific study of complex systems.

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Chief Editor, Prof Sayama, is currently researching complex dynamical networks, human and social dynamics, artificial life, and interactive systems while working at Binghamton University, State University of New York.

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Complexity publishes original research and review articles across a broad range of disciplines with the purpose of reporting important advances in the scientific study of complex systems.

Complexity