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Enhancing financial time series forecasting through topological data analysis

Neural Computing and Applications 2025年第9期37卷 6527-6545页

作者： de Jesus, Luiz Carlos Fernández-Navarro, Francisco Carbonero-Ruz, Mariano Department of Quantitative Methods Universidad Loyola Andalucía Córdoba41704 Spain Department of Computer Languages and Computer Science Universidad de Málaga Málaga29010 Spain

Topological data analysis (TDA) is increasingly acknowledged within financial markets for its capacity to manage complexity and discern nuanced patterns and structures. It has been applied effectively to uncover intricate relationships and capture non-linear dependencies inherent in market data. This manuscript presents a groundbreaking study that delves into integrating features derived from TDA to improve the performance of forecasting models for univariate time series prediction. The research specifically examines whether incorporating features extracted from TDA-such as entropy, amplitude, and the number of points obtained from persistent diagrams can provide valuable supplementary information to the baseline forecasting model. Thus, the aim is to determine if these TDA-derived features can boost forecasting accuracy by offering additional insights that existing models might overlook. The N-BEATS model serves as the baseline forecasting model due to its robust generalization capabilities and flexibility in incorporating additional features into the model. The proposed methodology is compared against a univariate N-BEATS model without additional features and other strategies incorporating supplementary features such as temporal decomposition and time delay embeddings. The evaluation includes forecasting for six cryptocurrencies across four distinct time scenarios and four traditional financial instruments across two scenarios each, resulting in 32 datasets. The results obtained were promising, as the proposed method, N-BEATS+TDA, achieved the best results in mean performance and mean ranking for the three metrics considered (MAPE, MAE, and RMSE). Significant differences were observed with the rest of the proposed methods using a significance level of α=0.10, highlighting the effectiveness of integrating TDA features to enhance forecasting models. © The Author(s) 2024.

关键词： Prediction models

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Two-Stage Shadow Inclusion Estimation: An IV Approach for Causal Inference under Latent Confounding and Collider Bias 41

Two-Stage Shadow Inclusion Estimation: An IV Approach for Ca...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Baohong Wu, Anpeng Xiong, Ruoxuan Kuang, Kun College of Computer Science and Technology Zhejiang University Hangzhou China Department of Quantitative Theory & Methods Emory University Atlanta United States

Latent confounding bias and collider bias are two key challenges of causal inference in observational studies. Latent confounding bias occurs when failing to control the unmeasured covariates that are common causes of treatments and outcomes, which can be addressed by using the Instrumental Variable (IV) approach. Collider bias comes from non-random sample selection caused by both treatments and outcomes, which can be addressed by using a different type of instruments, i.e., shadow variables. However, in most scenarios, these two biases simultaneously exist in observational data, and the previous methods focusing on either one are inadequate. To the best of our knowledge, no approach has been developed for causal inference when both biases exist. In this paper, we propose a novel IV approach, Two-Stage Shadow Inclusion (2SSI), which can simultaneously address latent confounding bias and collider bias by utilizing the residual of the treatment as a shadow variable. Extensive experimental results on benchmark synthetic datasets and a real-world dataset show that 2SSI achieves noticeable performance improvement when both biases exist compared to existing methods. Copyright 2024 by the author(s)

关键词： Colliding beam accelerators

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A High-Capacity Image Steganography Based on LSB Inversion and Summing of Bit Pairs 6

A High-Capacity Image Steganography Based on LSB Inversion a...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Mamdouh, Loay Bekhet, Saddam South Valley University Faculty of Computers and Artificial Intelligence Department of Computer Science Hurghada Egypt South Valley University Faculty of Commerce Department of Quantitative Methods Qena Egypt

ISBN: (纸本)9798350378511

Nowadays, computer users face a challenge in maintaining the security of their information. There are many techniques for transferring information safely over networks. Image steganography, one of these techniques, is an effective way to conceal important information. The Least Significant Bit (LSB) technique is a famous method for data hiding. This technique exchanges the LSBs of the cover image pixel with the mystery message bits. However, it was always the challenging problem of increasing the number of bits utilized for embedding that led to a decreased quality of the stego image. Hence, this research targets optimizing the stego image quality by inverting the LSBs of image pixels to reduce the changes that occur in pixels. This paper proposes a new method based on summing the pairs and inverting the LSBs of the cover image pixels. The secret data are arranged in pairs, and then the sum of every two successive pairs is computed. After that, the secret bits are embedded by inverting the pixel bits depending on that sum. Finally, for more enhancement of the results, some of the inverted bits will be inverted again. Rigorous experiments using benchmark datasets confirm that the proposed scheme yields superior results compared to the substitution LSB scheme. Also, shows the effectiveness of the proposed approach in achieving a high payload with acceptable visual quality for stego image that cannot be noticed by human eyes when compared with state-of-the-art steganography techniques. © 2024 IEEE.

关键词： Image quality

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A Large-Scale Group Decision-Making Approach Employing Large Language Models to Detect Assertive Groups 23

A Large-Scale Group Decision-Making Approach Employing Large...

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23rd International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2024

作者： Carbó-García, Santiago Trillo, José Ramón Tapia, Juan Miguel Pérez, Ignacio Javier Herrera-Viedma, Enrique Cabrerizo, Francisco Javier Andalusian Research Institute in Data Science and Computational Intelligence Department of Computer Science and Artificial Intelligence University of Granada Granada Spain Department of Quantitative Methods for Business and Economics University of Granada Granada Spain

ISBN: (纸本)9781643685380

Large-Scale Group Decision-Making, propelled by the advent of Large Language Models and the imperative for assertiveness in decision-making processes, emerges as a pivotal area of research. This paper navigates through the landscape of Large-Scale Group Decision-Making, delineating its significance across diverse domains, including social networks and e-democracy. Amidst its nascent status, Large-Scale Group Decision-Making encounters formidable challenges, particularly in information management and fostering consensus among a multitude of experts. This paper aims to illuminate the goals and hurdles facing Large-Scale Group Decision-Making approaches through an exhaustive review of contemporary literature and methodologies. By confronting these challenges head-on, Large-Scale Group Decision-Making holds the potential to redefine decision-making paradigms, bolster assertiveness, and elevate collective problem-solving capabilities in an increasingly interconnected world. © 2024 IOS Press. All rights reserved.

关键词： Information management

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Learning Shadow Variable Representation for Treatment Effect Estimation under Collider Bias 41

Learning Shadow Variable Representation for Treatment Effect...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Baohong Li, Haoxuan Xiong, Ruoxuan Wu, Anpeng Wu, Fei Kuang, Kun College of Computer Science and Technology Zhejiang University Hangzhou China Center for Data Science Peking University Beijing China Department of Quantitative Theory & Methods Emory University Atlanta United States

One of the significant challenges in treatment effect estimation is collider bias, a specific form of sample selection bias induced by the common causes of both the treatment and outcome. Identifying treatment effects under collider bias requires well-defined shadow variables in observational data, which are assumed to be related to the outcome and independent of the sample selection mechanism, conditional on the other observed variables. However, finding a valid shadow variable is not an easy task in real-world scenarios and requires domain-specific knowledge from experts. Therefore, in this paper, we propose a novel method that can automatically learn shadow-variable representations from observational data without prior knowledge. To ensure the learned representations satisfy the assumptions of the shadow variable, we introduce a tester to perform hypothesis testing in the representation learning process. We iteratively generate representations and test whether they satisfy the shadow-variable assumptions until they pass the test. With the help of the learned shadow-variable representations, we propose a novel treatment effect estimator to address collider bias. Experiments show that the proposed methods outperform existing treatment effect estimation methods under collider bias and prove their potential application value. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Autoencoder Neural Networks for Anomaly Detection in Wind Turbines 19th

Autoencoder Neural Networks for Anomaly Detection in Wind Tu...

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19th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Coronel, D. Guevara, C. Santos, M. Computer Science Faculty Complutense University of Madrid Madrid28040 Spain Quantitative Methods Department CUNEF University Madrid28040 Spain Institute of Knowledge Technology Complutense University of Madrid Madrid28040 Spain

ISBN: (纸本)9783031750090

Wind energy has emerged as a crucial solution in the search for sustainable and environmentally friendly energy sources. As global energy demand grows rapidly and the need to reduce greenhouse gas emissions increases, wind turbines have become a key technology for the transition to a cleaner and more sustainable energy future. This paper focuses on the application of autoencoder neural networks for the accurate and efficient detection of anomalies in wind turbines. The main objective is to significantly improve monitoring and predictive maintenance of wind turbines and thus enhance wind energy availability. The dataset used includes records of wind turbines with both, healthy and faulty conditions. Healthy records serve as a baseline to illustrate the expected amplitude of the turbine vibrations under optimal operation conditions. In contrast, failure records arising from blade surface erosion, blade unbalanced, and blade twist are used to evaluate the failure detection model. The proposed algorithm demonstrates a precision of 95.42% for blade unbalanced failures, 94.63% for blade surface erosion, and 87.54% for blade twist faults. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Windmill

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Comparative Analysis of COVID-19 Detection methods Based on Neural Network

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computers, Materials & Continua 2023年第7期76卷 1127-1150页

作者： Inès Hilali-Jaghdam Azhari A Elhag Anis Ben Ishak Bushra M.Elamin Elnaim Omer Eltag Mohammed Elhag Feda Muhammed Abuhaimed S.Abdel-Khalek Computer Sciences and Information Technology Programs Applied CollegePrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Mathematics and Statistics College of ScienceTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Quantitative Methods Higher Institute of ManagementUniversity of Tunis1073Tunisia Department of Computer Science College of Science and Humanities in Al-SulailPrince Sattam Bin Abdulaziz UniversityAl KharjSaudi Arabia Department of Computer Science Faculty of Science and ArtsKing Khalid UniversityAbha61421Saudi Arabia Department of Mathematics Faculty of ScienceSohag UniversitySohagEgypt

In 2019,the novel coronavirus disease 2019(COVID-19)ravaged the *** of July 2021,there are about 192 million infected people worldwide and 4.1365 million *** present,the new coronavirus is still spreading and circulating in many places around the world,especially since the emergence of Delta variant strains has increased the risk of the COVID-19 pandemic *** symptoms of COVID-19 are diverse,and most patients have mild symptoms,with fever,dry cough,and fatigue as the main manifestations,and about 15.7%to 32.0%of patients will develop severe *** are screened in hospitals or primary care clinics as the initial step in the therapy for *** transcription-polymerase chain reaction(PCR)tests are still the primary method for making the final diagnosis,in hospitals today,the election protocol is based on medical imaging because it is quick and easy to use,which enables doctors to diagnose illnesses and their effects more *** to this approach,individuals who are thought to have COVID-19 first undergo an X-ray session and then,if further information is required,a CT-scan *** methodology has led to a significant increase in the use of computed tomography scans(CT scans)and X-ray pictures in the clinic as substitute diagnostic methods for identifying *** provide a significant collection of various datasets and methods used to diagnose COVID-19,this paper provides a comparative study of various state-of-the-art *** impact of medical imaging techniques on COVID-19 is also discussed.

关键词： Neural networks frameworks intelligent systems COVID-19 CT

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Enhancing Password Security: Analyzing Password Management Practices Among IT Students 7

Enhancing Password Security: Analyzing Password Management P...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power Engineering, CANDO-EPE 2024

作者： Kollar, Anita Mihalovicsne Katona, Jozsef Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Hungary

ISBN: (纸本)9798331531119

Today, with the digital world in its full swing, strong password management is key to protecting sensitive information from both internal and external threats. The current research explores the passwords managing behavior of the IT students at the University of Dunaujvaros. A survey made in May 2024 questioned 68 students, 59 males and 9 females, about behaviors such as using passwords with complexity, not repeating them for multiple accounts or machines, changing them regularly, and which applications they use for password management. The findings have depicted that, as a whole, students appear to be moderately sensitive about the password security instructions. Nonetheless, most continue to indulge in unsafe practices, including easily remembered or weak passwords and infrequent password changes. Furthermore, approximately half of the responders use password management applications, indicating room for significant improvement in the future. The results suggest that intervention and special education measures should be taken to enhance the practice of password security among future IT experts. This research has good potential to contribute toward strategies designed for people behaving safely in password management, and hence promote a secure digital environment. © 2024 IEEE.

关键词： Students

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A Consensus-Based Multi-Criteria Group Decision-Making Method Based on an Aggregated Operator Customised by Experts 9

A Consensus-Based Multi-Criteria Group Decision-Making Metho...

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Trillo, José Ramón Cabrerizo, Francisco Javier Del Moral, María José Morente-Molinera, Juan Antonio Tapia, Juan Miguel Herrera-Viedma, Enrique University of Granada Department of Computer Science and Artificial Intelligence Granada18071 Spain University of Granada Granada18071 Spain University of Granada Department of Quantitative Methods for Business and Economics Granada18071 Spain

ISBN: (纸本)9798350311402

Group decision-making is a daily process where a group of experts has to choose from a set of alternatives. To help this group of experts, methods were developed that allowed them to recommend an alternative or a group of alternatives from the information generated by the experts, these methods were called Group Decision-Making methods. Nonetheless, some Group Decision-Making methods have some problems, such as evaluating alternatives in a general way. This problem leads to a loss of information because it is not possible to evaluate each alternative in detail and it may be the case that one alternative is better than another on one criterion but not on another. Another problem is that experts have priority for each criterion and for one expert one criterion may be more important than another and vice versa. To solve these two problems, in this novel method, we propose a consensual multi-criteria Group Decision-Making system, where experts can customise the order of the criteria. With the order of each expert, the method creates an aggregation operator for each criterion. Moreover, this method performs a consensus analysis for each criterion, so that there is a minimum consensus on each criterion. In this way, a solution is obtained that allows the set of alternatives to be evaluated in a detailed way and each criterion has the correct importance because the experts are the ones who create the order of importance. © 2023 IEEE.

关键词： Decision making

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Designing a Model for Predicting Asthma in Adolescent Using Map Reduce and Federated Learning

Designing a Model for Predicting Asthma in Adolescent Using ...

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2024 International Conference on science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ekpo, Raphael Henshaw Chukwudi Osamor, Victor Azeta, Ambrose A. Akindeji, Kayode Orelaja, Adeyinka Elizabeth Covenant University Department of Computer and Information Sciences Km 10 Idiroko Road Ogun State Ota Nigeria Namibia University of Science and Technology Department of Software Engineering Windhoek Namibia Durban University of Technology Faculty of Engineering and the Built Environment Department of Electrical Power Engineering South Africa Department of Computer Science and Quantitative Methods ClarksvilleTN United States

ISBN: (纸本)9798350358155

This study aims at designing Map Reduce and federated learning-based asthma prediction model for adolescent to provide answers to the problems associated with the existing asthma prediction model for adolescent. This study leveraged on two diverse datasets: the Nigerian Hospital Asthma dataset and the National Survey of Children Health dataset for benchmarking purposes. Symmetrical uncertainty and normalization interaction were employed for feature selection. The model was trained using Federated Artificial Intelligence (AI) Technology Enabler (FATE) and complemented with XGBoost model and one central server for federated algorithm averaging. The study was implemented with python programming language on Google Collaboratory environment. The results of the analysis showed considerable high accuracy of 0.98, precision (0.98%), recall (0.98%) and F1-socre (0.99%) for the asthmatic class and precision of 0.98%, recall of 0.98%, as well as F1-score of 0.99% for the non-asthmatic class. The implemented model was benchmark with the existing asthma prediction models in the literature. Simulated attack was also performed using implemented model with and without Map Reduce. This study concludes that the researchers' model has the potentials to outperform some of the existing machine learning models on asthma prediction in adolescent, thereby facilitating collaborative sharing of the model with other hospitals, protect the patient information from leakage, meeting the criteria of healthcare data protection regulation as well as beneficial for enhancing the security of the dataset. © 2024 IEEE.

关键词： Federated learning

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