MUMBAI, India, Sept. 26 -- Intellectual Property India has published a patent application (202441022703 A) filed by Jennifer D; Dheepan Balaji; Gowtham. S; and Sudharsanan. R, Chennai, Tamil Nadu, on March 23, 2024, for 'employing seasonal decomposition for dynamic stock price predication within the realm of web-based applications.'
Inventor(s) include Jennifer D; Dheepan Balaji; Gowtham. S; and Sudharsanan. R.
The application for the patent was published on Sept. 26, under issue no. 39/2025.
According to the abstract released by the Intellectual Property India: "This research endeavor constitutes an intricate fusion of advanced quantitative methodologies within a cutting-edge web-based predictive analytics framework tailored for stock market forecasting. The amalgamation of fundamental analysis metrics encompasses a meticulous examination of financial ratios, earnings reports, and economic indicators, affording a nuanced comprehension of underlying asset valuations. Concurrently, the incorporation of elaborate technical indicators scrutinizes intricate market trends, oscillators, and moving averages, offering a granular perspective on potential entry and exit points for investors. Augmenting this analytical prowess is the integration of sentiment analysis, where OpenAI's state-of-the-art language models decode market sentiment from an expansive corpus of real-time stock news. This linguistic analysis discerns subtle nuances, capturing market sentiment dynamics and providing an additional layer of insight for predictive modeling. Furthermore, the research introduces a pioneering approach to time series analysis, leveraging sophisticated algorithms to dissect historical stock data. This entails the identification of latent patterns, trends, and seasonality, significantly enhancing the temporal dimension of predictive modeling. The holistic synergy of these diverse quantitative models unfolds within a unified web application, constituting a transformative paradigm in algorithmic trading and investment strategies. In conclusion, empirical analyses validate the efficacy of this integrative approach, substantiating its potential to redefine the landscape of predictive analytics within the dynamic and complex milieu of stock market prognostication. This comprehensive platform not only enhances the precision of market predictions but also provides investors and traders with a sophisticated toolkit for navigating the intricacies of modern financial markets with informed and strategic decision-making."
Disclaimer: Curated by HT Syndication.
