Exploring the B-Spline Transform for Estimating Lévy Process Parameters: Applications in Finance and Biomodeling Exploring the B-Spline Transform for Estimating Lévy Process Parameters: Applications in Finance and Biomodeling Letters in Biomathematics · Jul 7, 2025Letters in Biomathematics · Jul 7, 2025 Show publication This paper, presents the application of the B-spline transform as an effective and precise technique for estimating key parameters i.e., drift, volatility, and jump intensity for Lévy processes. Lévy processes are powerful tools for representing phenomena with continuous trends with abrupt changes. The proposed approach is validated through a simulated biological case study on animal migration in which movements are modeled as Lévy flights with long-range jumps and directionally biased drift. This scenario depicts real-world stochastic behaviors in the spatial dynamics of a species. The results demonstrate the power of the B-spline method in its capability to accommodate complex stochastic behaviors with low mean squared error (MSE). To demonstrate its relevance in an actual financial context, the model is applied to forecast trends in Iraqi ATM usage based on data collected between the years 2008 and 2021. The results indicate a uniform growth in demand, supported by forecasts for the years 2022 and 2023, confirming the model’s predictive accuracy. Overall, the research identifies the B-spline transform as a robust method for parameter estimation in Lévy-based models with potential applications in finance, ecology, and biomathematics.
The study aims to identify the uses and the impact of social networking applications and websites on stock markets and their role in defining the details of dealing with stock movement and trading. The study also aims to highlight the role of these networks by increasing confidence in stock markets and companies as well as encouraging and inciting young people to invest in these markets, the study belongs to the descriptive analytical approach, the study population consisted of all current and potential investors in the stock and financial markets in the United Arab Emirates. The study used a questionnaire that was distributed to a number of followers of social networking pages and websites that deal with trading
... Show MoreDeveloping and researching antenna designs are analogous to excavating in an undiscovered mine. This paper proposes a multi-band antenna with a new hexagonal ring shape, theoretically designed, developed, and analyzed using a CST before being manufactured. The antenna has undergone six changes to provide the best performance. The results of the surface current distribution and the electric field distribution on the surface of the hexagonal patch were theoretically analyzed and studied. The sequential approach taken to determine the most effective design is logical, and prevents deviation from the work direction. After comparing the six theoretical results, the fifth model proved to be the best for making a prototype. Measured results rep
... Show MoreAlthough renewable energy systems have become an interesting global issue, it is not continuous either daily or seasonally. Latent heat energy storage (LHES) is one of the suitable solutions for this problem. LHES becomes a basic element in renewable energy systems. LHES compensate for the energy lack when these systems are at low production conditions. The present work considered a shell and tube LHES for numerical investigation of the tube rotation influence on the melting process. The simulation and calculations were carried out using ANSYS Fluent software. Paraffin wax represents the phase change material (PCM) in this work, while water was selected to be the heat transfer fluid (HTF). The calculations were carried o
... Show MoreThyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise
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