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 Abhijith, E.; Ramasamy, Gobi; Saravanan, K.N. Title A name given to the resource Dynamic Financial Portfolio Optimization Using Temporal Convolutional Networks and Real-Time Data Analysis 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 Proceedings of 2025 International Conference on Emerging Technologies in Computing and Communication, ETCC 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ETCC65847.2025.11108346" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ETCC65847.2025.11108346</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015832167?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015832167?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 Abhijith E., Department of Computer Science, Christ University, Bangalore, India; Ramasamy G., Department of Computer Science, Christ University, Bangalore, India; Saravanan K.N., Department of Computer Science, Christ University, Bangalore, India Description An account of the resource This paper presents an integrated framework for AI-driven portfolio optimization combining temporal convolutional networks (TCNs) with conditional value-at-risk (CVaR) minimization. Our system processes real-time market data through an automated pipeline implementing volatility-adjusted feature engineering and walk-forward validation. The architecture employs dilated causal convolutions for temporal pattern extraction combined with Ledoit-Wolf shrinkage covariance estimation for robust portfolio optimization. Experimental results demonstrate an 18.7% annualized return with 22.3% volatility, outperforming traditional mean-variance optimization by 14.2% in risk-adjusted returns. The implementation addresses key challenges in numerical stability and computational efficiency through eigenvalue clamping and gradient checkpointing. 2025 IEEE. Subject The topic of the resource algorithmic trading; Portfolio optimization; real-time data analysis; risk management; temporal convolutional networks Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152476-0; 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