-
2024Zakia Zouaghia, Zahra Kodia, Lamjed Ben Said
Predicting the stock market prices using a machine learning-based framework during crisis periods
Multimed Tools Appl 84, 28873–28907., 2024
Abstract
Stock markets are highly volatile, complex, non-linear, and stochastic. Therefore, predicting stock market behavior is one of finance’s most complex challenges. Recently, political, health, and economic crises have profoundly impacted global stock prices, leading researchers to use machine learning to predict prices and analyze financial data. This article delves into two primary facets: firstly, examining stock price responses to the Russia-Ukraine war and the COVID-19 pandemic by assessing volatility and draw-downs from 2018 to 2023. Secondly, we introduce a framework named StockPredCris, designed to predict future stock indices employing two machine learning algorithms: Support Vector Regression (SVR) and eXtreme Gradient Boosting (XGBoost). Our experiments are conducted on four major stock market indices (NASDAQ, Russell 2000, DAX, and SSE) using a combination of historical stock index data and COVID-19 pandemic data. The performance of the implemented models is evaluated using five regression metrics along with the CPU time metric. The results of SVR demonstrates superior performance and signifcantly outperforms the other considered models for benchmarking. For instance, SVR achieved 0.0 MSE and 99.99% R for the four stock indices, with training times between 0.005 and 0.007 seconds. We recommend SVR for predicting future stock prices during crises. This study offers valuable insights for financial decision-makers, guiding them in making informed choices by understanding stock market reactions to major global crises, while addressing the uncertainty and fear of investors.
Zakia Zouaghia, Zahra Kodia, Lamjed Ben SaidA collective intelligence to predict stock market indices applying an optimized hybrid ensemble learning model
In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2024. Lecture Notes in Computer Science(), vol 14810. Springer, Cham., 2024
Abstract
The stock market which is a particular type of financial market, has attracted nowadays the attention of financial analysts and investors, as it is recognized one of the most challenging and unpredictable market. Recently, this kind of market is well known by its extreme volatility and instability due to the health (COVID-19), the geopolitical (the Russian, Ukraine, European, and American conflict), and the economic crises. This situation intensified the uncertainty and fear of investors; they need an intelligent and stable decision support system to assist them to foresee the future variations of stock prices. To address this issue, our paper proposes a hybrid ensemble-learning model that integrates different methods. (1) The Singular Spectrum Analysis (SSA) is used to eliminate the noise from financial data. (2) The Convolutional Neural Network (CNN) is applied to handle the task of automatic feature extraction. (3) Three machine-learning predictors (Random Forest (RF), Gradient Boosting (GB), and Extra Trees (ET)) are merged together and optimized using the Halving Grid Search (HGS) algorithm to obtain collective final predictions. To verify the validity of the proposed model, two major indices of Chinese and American stock markets, namely SSE and NASDAQ, were used. The proposed model is evaluated using RMSE, MAE, MAPE and CPU time metrics. Based on experiments, it is proven that the achieved results are better than other comparative prediction models used for benchmarking.
Zakia Zouaghia, Zahra Kodia, Lamjed Ben SaidPred-ifdss: An intelligent financial decision support system based on machine learning models
2024 10th International Conference on Control, Decision and Information Technologies (CoDIT), Vallette, Malta, 2024, pp. 67-72, 2024
Abstract
Financial markets operate as dynamic systems susceptible to ongoing changes influenced by recent crises, such as geopolitical and health crises. Due to these factors, investor uncertainty has increased, making it challenging to identify trends in the stock markets. Predicting stock market prices enhances investors’ ability to make accurate investment decisions. This paper proposes an intelligent financial system named Pred-IFDSS, aiming to recommend the best model for accurate predictions of future stock market indexes. Pred-IFDSS includes seven machine learning models: (1) Linear Regression (LR), (2) Support Vector Regression (SVR), (3) eXtreme Gradient Boosting (XGBoost), (4) Simple Recurrent Neural Network (SRNN), (5) Gated Recurrent Unit (GRU), (6) Long Short-Term Memory (LSTM), and (7) Artificial Neural Network (ANN). Each model is tuned using the grid search strategy, trained, and evaluated. Experiments are conducted on three stock market indexes (NASDAQ, S&P 500, and NYSE). To measure the performance of these models, three standard strategic indicators are employed (MSE, RMSE, and MAE). The outcomes of the experiments demonstrate that the error rate in SRNN model is very low, and we recommend it to assist investors in foreseeing future trends in stock market prices and making the right investment decisions.
Abir Chaabani, Lamjed Ben SaidSolving Hierarchical Production–Distribution Problem Based on MDVRP Under Flexibility Depot Resources in Supply Chain Management
In: Alharbi, I., Ben Ncir, CE., Alyoubi, B., Ben-Romdhane, H. (eds) Advances in Computational Logistics and Supply Chain Analytics. Unsupervised and Semi-Supervised Learning. Springer, Cham,129--147.., 2024
Abstract
Bi-level optimization problems (BLOPs) is a class of challenging problems with two levels of optimization tasks. The particular structure of the bi-level optimization model facilitates the formulation of several practical situations that involve hierarchical decision-making process where lower-level decisions depend on upper-level actions. In this context, a hierarchical production–distribution (PD) planning problem in supply management is addressed. These two entities (production and distribution) are naturally related; however, in most practical situations, each decision entity concentrates on optimizing its process one at a time, independently on other related decisions. In this chapter, we considered a new formulation of the PD system using the bi-level framework under the constraints of shared depots resources in the distribution phase. To this end, a mixed integer bi-level formulation is proposed to model the problem, and a cooperative decomposition-based algorithm is developed to solve the bi-level model. Statistical experimental results show that our proposed algorithm gives competitive and better results with respect to the competitor algorithm. Indeed, allowing flexible choice of the stop depot reveals the outperformance of our proposal in reducing total traveling cost of generated solution compared to the baseline problem.
Lilia Rejeb, Abir Chaabani, , Lamjed Ben SaidMultimodal freight transport optimization based on economic and ecological constraint
. In: Alharbi, I., Ben Ncir, CE., Alyoubi, B., Ben-Romdhane, H. (eds) Advances in Computational Logistics and Supply Chain Analytics. Unsupervised and Semi-Supervised Learning. Springer, Cha, 2024
Abstract
The increasing demand for efficient global supply chain management and faster product delivery has led to a rise in the use of multimodal transportation systems (MFT). One of the key challenges in multimodal transportation is selecting the appropriate freight mode. This decision depends on several factors such as cost, transit time, reliability, mode availability, service frequency, and cargo characteristics. However, existing research often focuses on only two modes, namely trucks and trains, which fails to capture the complexities of real-world freight transportation decisions. Moreover, while reducing travel time and cost are primary objectives for service providers and researchers, other important considerations such as environmental impact are often overlooked. To this end, in this work, the researchers take into account four major modes of transportation (Air, Road, Rail, and Sea/Water) in a multimodal freight context aiming to optimize three distinct objectives: overall transportation cost, transportation time, and CO2 emissions. To solve this problem, the researchers adopt two the well-known metaheuristic algorithms: Tabu Search and the Genetic Algorithm through an experimental study demonstrating the efficacy of these evolutionary solution methods in tackling such challenging optimization problems.
Abir Chaabani, , Lamjed Ben SaidA New Bi-level Modeling for the Home Health Care Problem Considering Patients Preferences
International Conference on Control Decision and Information Technology Codit’10, Vallette, Malta, 2721-2726, 2024
Abstract
Home Health Care (HHC) aims to provide medical care and support services directly to patients in their own homes. The demand for HHC services is steadily increasing due to demographic trends, with a growing preference for receiving care in the home. This trend pushes organizations providing home health care services, to optimize their activities in order to meet this increasing demand efficiently. For this purpose, we propose in this work a new bi-level modeling of the problem, that we termed Bi-level Home Health Care Problem Considering Patients Preferences (Bi-HHCPP) aiming to find an efficient solution corresponding to this design. Existing research studies have focused on optimizing the problem considering only one decision-maker that optimizes both routing and scheduling entities imposed by the problem. This paper is the first to shed light on a new bi-level modeling of the problem involving two hierarchical decision entities: (1) a scheduling entity, and (2) a routing one. The proposed model primarily accounts for nurse qualification, travel costs, and patient preferences on visited nurses. Besides, the proposed mathematical formulation of the problem is tested using the CBC (Coin-or Branch and Cut) optimization solver.
, Abir Chaabani, Nadia Ben Azzouna,Hybrid genetic algorithm for solving an online vehicle routing problem with time windows and heterogeneous fleet
23rd International Conference on Hybrid Intelligent Systems (HIS'23), 437-446, Springer Nature Switzerland, 2024
Abstract
The Vehicle Routing Problem (VRP) is a well-known optimization problem in which we aim traditionally to minimize transportation costs while satisfying customer demands. In fact, most logistics companies use a heterogeneous fleet with varying capacities and costs, presenting a more complex variant known as Rich VRP (RVRP). In this paper, we present a mathematical formulation of the RVRP, considering both hard time windows and dynamically changing requests to be as close as possible to real-life logistics scenarios. To solve this challenging problem, we propose a Hybrid Genetic Algorithm (HGA). The experimental study highlights the out-performance of our proposal when evaluated alongside other algorithms on the same benchmark problems. Additionally, we conduct a sensitivity analysis to illustrate how resilient the algorithm is when problem parameters are altered.
Thouraya Sakouhi,Clustering-based multidimensional sequential pattern mining of semantic trajectories
International Journal of Data Mining, Modelling and Management, 16(2), 148-175., 2024
Abstract
Knowledge discovery from mobility data is about identifying behaviours from trajectories. In fact, mining masses of trajectories is required to have an overview of this data, notably, investigate the relationship between different entities movement. Most state-of-the-art work in this issue operates on raw trajectories. Nevertheless, behaviours discovered from raw trajectories are not as rich and meaningful as those discovered from semantic trajectories. In this paper, we establish a mining approach to extract patterns from semantic trajectories. We propose to apply sequential pattern mining based on a pre-processing step of clustering to alleviate the former’s temporal complexity. Mining considers the spatial and temporal dimensions at different levels of granularity providing then richer and more insightful patterns about humans behaviour. We evaluate our work on tourists semantic trajectories in Kyoto. Results showed the effectiveness and efficiency of our model compared to state-of-the-art work.
Moez Elarfaoui, Nadia Ben AzzounaCLUSTERING BASED ON HYBRIDIZATION OF GENETIC ALGORITHM AND IMPROVED K-MEANS (GA-IKM) IN AN IOT NETWORK
International Journal of Wireless & Mobile Networks (IJWMN), Vol.16, No.6, December 2024, 2024
Abstract
The continuous development of Internet infrastructures and the evolution of digital electronics, particularly Nano-computers, are making the Internet of Things (IoT) emergent. Despite the progress, these IoT objects suffer from a crucial problem which is their limited power supply. IoT objects are often deployed as an ad-hoc network. To minimize their consumption of electrical energy, clustering techniques are used. In this paper, a centralized clustering algorithm with single-hop routing based on a genetic algorithm and Improved k-means is proposed. The proposed approach is compared with the LEACH, K-means and OK-means algorithms. Simulation results show that the proposed algorithm performs well in terms of network lifetime and energy consumption.
Alia Maaloul, Meriam Jemel, Nadia Ben AzzounaXAI based feature selection for gestational diabetes Mellitus prediction
10th International Conference on Control, Decision and Information Technologies (CoDIT), Vallette, Malta, 2024, pp. 1939-1944, 2024
Abstract
Gestational Diabetes Mellitus (GDM) is a type of diabetes that develops during pregnancy. It is important for pregnant women to monitor their blood sugar levels regularly and follow a healthy diet. However, early intervention can greatly reduce risk of this type of diabetes. Machine Learning and Deep Learning techniques are utilized to predict this risk based on an individual’s symptoms, lifestyle, and medical history. By identifying key features such as age, insulin, body mass index, and glucose levels, machine learning models such as Random Forest and XGBoost are used in this research work to classify patients at risk of a gestational diabetes. In addition, we propose an explainable feature selection approach to improve the accuracy of machine learning models for GDM prediction. This method involves iteratively eliminating features that exhibit a negative contribution as determined by the SHAP (Shapley Additive explanations) feature attribution explanations for the model’s predictions
