TY - GEN
T1 - AI-Powered Dashboard for User-Centric Financial Data Analytics and Intelligent Stock Prediction
AU - Aladakatti, Shweta S.
AU - Madhura, K.
AU - Prusty, K. Soveet Kumar
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - The financial markets are extremely dynamic and non-linear, predicting stock values is a challenging undertaking. Conventional statistical techniques, such as linear regression and ARIMA, frequently fall short of capturing the intricate temporal dependencies and obscure connections that control market conditions. To overcome these constraints, this study suggests a dashboard framework driven by AI that smoothly combines sophisticated deep learning models with a system for user-centered visualization and interaction. The framework makes it possible to collect, preprocess, and normalize data from live sources like Yahoo Finance. This research prepares a comprehensive framework, paired with an API readily exposed to a graphical user-interface aimed at creating, training and comparing multiple neural network architectures over configurable parameter spaces, and a visual comparison to discern peculiarities and assess model performances over real data sourced live from the internet. In addition to providing opportunities for future developments in risk modeling and multimodal forecasting, the implementation shows great promise for real-world deployment in financial technology ecosystems.
AB - The financial markets are extremely dynamic and non-linear, predicting stock values is a challenging undertaking. Conventional statistical techniques, such as linear regression and ARIMA, frequently fall short of capturing the intricate temporal dependencies and obscure connections that control market conditions. To overcome these constraints, this study suggests a dashboard framework driven by AI that smoothly combines sophisticated deep learning models with a system for user-centered visualization and interaction. The framework makes it possible to collect, preprocess, and normalize data from live sources like Yahoo Finance. This research prepares a comprehensive framework, paired with an API readily exposed to a graphical user-interface aimed at creating, training and comparing multiple neural network architectures over configurable parameter spaces, and a visual comparison to discern peculiarities and assess model performances over real data sourced live from the internet. In addition to providing opportunities for future developments in risk modeling and multimodal forecasting, the implementation shows great promise for real-world deployment in financial technology ecosystems.
UR - https://www.scopus.com/pages/publications/105037504662
UR - https://www.scopus.com/pages/publications/105037504662#tab=citedBy
U2 - 10.1007/978-3-032-22065-3_10
DO - 10.1007/978-3-032-22065-3_10
M3 - Conference contribution
AN - SCOPUS:105037504662
SN - 9783032220646
T3 - Communications in Computer and Information Science
SP - 137
EP - 147
BT - Soft Computing and Its Engineering Applications - 7th International Conference, icSoftComp 2025, Proceedings
A2 - Patel, Kanubhai K.
A2 - Patel, Atul
A2 - Santosh, KC
A2 - Gomes de Oliveira, Gabriel
A2 - Ghosh, Ashish
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th International Conference on Soft Computing and its Engineering Applications, icSoftComp 2025
Y2 - 9 December 2025 through 11 December 2025
ER -