Spiking Neural Networks Optimized by Improved Cuckoo Search Algorithm: A Model for Financial Time Series Forecasting

The inherent challenges of financial time series forecasting demand advanced modeling techniques for reliable predictions. Effective financial time series forecasting is crucial for financial risk management and the formulation of investment decisions. The accurate prediction of stock prices is a subject of study in the domains of investing and national policy. This problem appears to be challenging due to the presence of multi-noise, nonlinearity, volatility, and the chaotic nature of stocks. This paper proposes a novel financial time series forecasting model based on the deep learning ensemble model LSTM-mTrans-MLP, which integrates the long short-term memory (LSTM) network, a modified Transformer network, and a multilayered perception (MLP). By integrating LSTM, the modified Transformer, and the MLP, the suggested model demonstrates exceptional performance in terms of forecasting capabilities, robustness, and enhanced sensitivity. Extensive experiments are conducted on multiple financial datasets, such as Bitcoin, the Shanghai Composite Index, China Unicom, CSI 300, Google, and the Amazon Stock Market. The experimental results verify the effectiveness and robustness of the proposed LSTM-mTrans-MLP network model compared with the benchmark and SOTA models, providing important inferences for investors and decision-makers.

In wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an improved cuckoo search (ICS) algorithm is proposed. This algorithm is based on the traditional cuckoo search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.

Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems. However, the deep learning algorithms often contain some hyper-parameters which may be continuous, integer, or mixed, and are often given based on experience but largely affect the effectiveness of activity recognition. In order to adapt to different hyper-parameter optimization problems, our improved Cuckoo Search(CS) algorithm is proposed to optimize the mixed hyper-parameters in deep learning algorithm. The algorithm optimizes the hyper-parameters in the deep learning model robustly, and intelligently selects the combination of integer type and continuous hyper-parameters that make the model optimal. Then, the mixed hyper-parameter in Convolutional Neural Network (CNN), Long-Short-Term Memory(LSTM) and CNN-LSTM are optimized based on the methodology on the smart home activity recognition datasets. Results show that the methodology can improve the performance of the deep learning model and whether we are experienced or not, we can get a better deep learning model using our method.

The research aims to improve prediction accuracy for heights of fractured water-conducting zones (FWCZs) and effectively prevent and control roof water disasters, to ensure safe coal mining. For this purpose, the method that integrates the improved cuckoo search (ICS) algorithm and extreme learning machine (ELM) is used to predict heights of FWCZs. Based on an analysis of factors influencing FWCZs, the ICS algorithm is employed to optimize two key parameters of the ELM model, the input weight ѡ and the bias b of hidden elements, thus establishing the ICS–ELM model for predicting the height of the FWCZ. The ICS–ELM model is trained using 42 measured samples, and the trained model is employed to predict the remaining six sample data points. The obtained prediction results show a relative error of only 3.97% and are more consistent with the actual situation. To verify the effectiveness of the model, the prediction results are compared with those of the adaptive particle swarm optimization based least squares support vector machine (APSO–LSSVM) and particle swarm optimization (PSO) based backpropagation (PSO–BP) models. The average relative errors of the two models are 8.21 and 9.75%, respectively, which further proves that the ICS–ELM model improves the accuracy of prediction results for heights of FWCZs. The heights of FWCZs predicted using the model are accurate and reliable, and the accuracy meets the requirements of engineering practice.

In this paper, a time series forecasting approach by integrating particle swarm optimization (PSO) and support vector regression (SVR) is proposed. SVR has been widely applied in time series predictions. However, no general guidelines are available to choose the free parameters of an SVR model. The proposed approach uses PSO to search the optimal parameters for model selections in the hope of improving the performance of SVR. In order to evaluate the performance of the proposed approach, the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) closing cash index is used as the illustrative example. Experimental results show that the proposed model outperforms the traditional SVR model and provides an alternative in financial time series forecasting.

The cuckoo search (CS) algorithm, which is a new meta-heuristic optimization algorithm, is an efficient approach to solve continuous function optimization problems. Normally, the parameters of the CS are fixed constants. This may result in affecting the algorithm efficiency. To cope with this issue, we properly tune the parameters of the CS and propose an adaptive cuckoo search (ACS) algorithm to enhance the convergence rate and accuracy of the CS. Then, we validate the proposed ACS against test functions and compare its performance with those of particle swarm optimization (PSO), improved cuckoo search algorithm (ICS) and CS by simulation. The simulation results demonstrate that the ACS obtains some solutions better than those obtained by PSO, ICS and CS.

This paper proposes an improved cuckoo search (ICSA) for solving the distribution network reconfiguration (NR) problem with multi-objective function. The membership functions are considered consisting of minimizing of power loss, load balancing among branches and among the feeders, node voltage deviation and switching operation numbers. ICSA is developed from the original CSA with adding the local search mechanism for exploiting around the current best solution. The effectiveness of the ICSA has validated on the 70-node and the 83-node practical systems. The obtained results have been compared to those from runner root algorithm (RRA) and other methods in the literature. The obtained results demonstrate that ICSA has high ability for searching the optimal solution with higher successful rate and better quality of obtained solution as well as smaller iterations compared to RRA and other methods. Therefore, ICSA is a reliable method for the multi-objective NR problems.

Security of the media information such as image and video is one of the fundamental prerequisites for broadcasting communications and computer systems. Most of image security system, for famous systems, similar to the Internet, is not reasonable for wireless sensor systems, requesting legitimate examination around there. In the proposed investigation, the security of Digital Images (DI) is upgraded by utilizing a Lightweight encryption algorithm. To ensure security, the images selected for security investigation was shared by a number of copies. With the help of the presented share creation model, for example, Chinese Remainder Theorem (CRT), the image was encoded into a number of shares and the created shares were highly secured by the proposed Stream Encryption (SE) algorithm. The metaheuristic algorithm was introduced by passing the selection of optimal public and private keys to encrypt as well as decrypt the image in secure transmission. With the help of Improved Cuckoo Search Algorithm (ICSA), the optimal keys were selected with fitness function as maximum throughput. The proposed SE-based ICSA algorithm consumed the least time in creating key value to decrypt the image in WSN security model. The simulation result exhibited that the SE-based ICSA algorithm enhanced the exactness of DI security for all input images (Lena, Barbara, baboon, house, and airplane) when compared with existing algorithms.

This literature review provides a comprehensive overview of key developments in quantitative trading theory, machine learning-based financial time series forecasting, deep learning-based financial time series forecasting, and modern quantitative investment strategies. It highlights seminal contributions from renowned scholars and researchers in the field. This review first explores the Efficient Market Hypothesis (EMH) proposed by Eugene Fama in 1970 and its empirical validation, which lays the foundation for understanding stock market dynamics. It then focuses on the application of machine learning to financial time series forecasting, including Hulls Delta strategy, Hujll Js multifactor model regression series, and Junhua Chens seminal work in 2009, which emphasizes the role of machine learning in solving the challenges of financial time series data. Finally, an overview of the development of deep learning in financial time series forecasting is presented, including the comprehensive model of Bowie et al., the success of Junhua Chens Deep Belief Network (DBN), and the sequence data processing capabilities of the Multi-stage Attention Network (MAN) model. In terms of modern quantitative investment strategies, it covers research areas such as EBIT/EV-based stock ranking studies, higher-order moment analysis, frequent trading strategies, and investor sentiment indicators. In summary, this literature review showcases the evolution of quantitative trading theory, the emergence of machine learning and deep learning in financial time series forecasting, and the development of modern quantitative investment strategies, offering valuable insights and tools for investors, researchers, and practitioners in financial markets.

The trajectory planning time is uncertain when there is no velocity constraint in the joint space of an operating robot. This paper proposes a 3-5-3 polynomial interpolation trajectory planning algorithm based on improved cuckoo search algorithm (ICS) which functions under a velocity constraint. Kinematics analysis of the robot is conducted and the 3-5-3 polynomial interpolation function is established as the foundation for trajectory planning. The size of the step size control factor is set as a variant which varies with the number of iterations, which accelerates the algorithm’s convergence and prevents premature falling into the local optimal solution, both problems in the traditional cuckoo search algorithm where the step size control factor is fixed. The proposed algorithm was used to optimize the trajectory of a test robot with interpolation time as the search space for optimization. Any interpolation time that did not meet the speed constraint was eliminated to secure the shortest possible robot running time. The UR robot was taken as a research object to simulate the joint motion trajectory planning in MATLAB. The proposed algorithm performed well and better realized the time-optimal trajectory under the constraint of speed compared to the traditional cuckoo search algorithm, particle swarm optimization algorithm, and genetic algorithm.

This paper concerns the problem of dynamical identification for an industrial robot manipulator and presents an identification procedure based on an improved cuckoo search algorithm. Firstly, a dynamical model of a 6-DOF industrial serial robot has been derived. And a nonlinear friction model is added to describe the friction characteristic at motion reversal. Secondly, we use a cuckoo search algorithm to identify the unknown parameters. To enhance the performance of the original algorithm, both chaotic operator and emotion operator are employed to help the algorithm jump out of local optimum. Then, the proposed algorithm has been implemented on the first three joints of the ER-16 robot manipulator through an identification experiment. The results show that (1) the proposed algorithm has higher identification accuracy over the cuckoo search algorithm or particle swarm optimization algorithm and (2) compared to linear friction model the nonlinear model can describe the friction characteristic of joints better.

Aiming at the problem of weak exploitation ability of Cuckoo Search (CS) algorithm, this paper proposes a Cuckoo Search algorithm with escape mechanism (ECS). First, we introduce a search strategy that enables candidate solution move closer to the optimal. Then, we design an escape mechanism based on backward learning. Numerical tests were conducted with the results showing a great improvement in the convergence speed and accuracy of the proposed ECS compared with the original.KeywordsCuckoo search algorithmBackward learningEscape mechanismConvergence performance

Identification of transient boundary conditions with improved cuckoo search algorithm and polynomial approximation

Financial time series forecasting model based on CEEMDAN and LSTM

A rate-dependent asymmetric Prandtl–Ishilinskii (RAPI) model was proposed to tackle the serious rate-dependent hysteresis nonlinearity of the giant magnetostrictive actuator(GMA) output. First, a polynomial function was introduced based on the PI model, and hysteresis factors were introduced to the Play operator, which accurately described the asymmetrical characteristic of the actuator output. On this basis, rate-dependent parameters were added to establish a rate-dependent RAPI model. Second, an improved cuckoo search (ICS) algorithm was proposed to solve the difficulty in the parameter identification of the RAPI model. For the ICS algorithm, the algorithm stability and optimization accuracy were improved using the adaptive step (AS) strategy and bird’s nest disturbance strategy. Then, the effectiveness of the ICS algorithm was tested by comparing it with other parameter identification algorithms. Finally, the rate-dependent RAPI model was verified by combining the output data of the giant magnetostrictive actuator under different frequencies. The results show that the rate-dependent RAPI model exhibits a higher accuracy than the PI model, thus verifying the effectiveness of the rate-dependent RAPI model.