Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Chaudhari, Mahek; John, Nimitha Title A name given to the resource Predicting Financial Market Volatility Using Regression and Machine Learning Techniques Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE 1st International Conference on Innovations in Engineering and Next-Generation Technologies for Sustainability, ICINVENTS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICINVENTS64613.2025.11401501" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICINVENTS64613.2025.11401501</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036987499?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036987499?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chaudhari M., CHRIST (Deemed to be University), India; John N., CHRIST (Deemed to be University), India Description An account of the resource In standard Simple Linear Regression (SLR), one of the major assumptions is that the error terms have constant variance (homoscedasticity). However, this assumption is frequently violated in many real-world datasets, resulting in inefficient estimates and reduced predictive accuracy. To overcome this shortcoming, we propose a hybrid modeling platform that combines SLR with statistical and machine learning methods. The approach starts with SLR to identify the main linear relationship. Whenever residual diagnostics report the presence of heteroskedasticity, an Autoregressive Conditional Heteroskedasticity (ARCH) model is used to estimate time-varying variance. Such estimated variances are utilized in a Weighted Generalized Least Squares (WGLS) model, which stabilizes the error structure. Finally, to capture any remaining nonlinear patterns, an Artificial Neural Network (ANN) is applied on the residuals of the WGLS model. By layering these techniques, the hybrid framework improves both stability and predictive power. Simulation studies and empirical tests on Apple Inc. stock data confirmed that the hybrid framework yields reduced MAE and RMSE values and greater explanatory strength than individual approaches. 2025 IEEE. Subject The topic of the resource Artificial Neural Networks (ANN); Autoregressive Conditional Heteroskedasticity (ARCH); Hybrid Modeling; Regression Models; Time Series; Weighted Generalized Least Squares (WGLS) Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155662-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online