An End-to-End Architecture for Stock Market Prediction Integrating Mobile Application, Backend Services, and ML/DL Models
DOI:
https://doi.org/10.21512/ijcshai.v3i1.15154Keywords:
Stock Market Prediction, End-to-End System, Machine Learning Models, Backend Services, Mobile Application, Data-Driven ApplicationAbstract
Prior research on stock market prediction has predominantly focused on algorithmic accuracy, leaving a significant research gap in the system-level realization required for real-world delivery. This paper addresses this disparity by presenting an end-to-end stock prediction delivery system that operationalizes trained machine learning models within a mobile-centric architecture. Unlike model-centric studies limited to offline evaluation, this work focuses on the rarity of system-level implementation. Market data are periodically ingested into a managed relational database, where predictions are generated using a fixed historical window and persisted for downstream access. A cross-platform mobile application serves as the primary user interface, providing structured access to historical prices, predictions, and accuracy metrics via backend APIs without local model inference. A key novelty is the implementation of an in-memory caching layer to optimize responsiveness for repeated mobile access. Experimental results demonstrate that this architecture significantly improves efficiency, reducing average API response times by approximately 94% from 817 ms to 48,7778 ms compared to direct database queries. These findings underscore the critical role of mobile-oriented system design in bridging the gap between predictive modeling and practical deployment.
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