Adaptive Model Selection in Stock Market Prediction: A Modular and Scalable Big Data Analytics Approach
Bibliographic record
Abstract
This paper introduces an innovative architecture integrating Apache Kafka and microservices to enhance realtime stock market prediction.Our approach dynamically selects the most effective predictive model based on current market conditions, ensuring consistent accuracy.The key research method involves deploying Apache Kafka for real-time data streaming, coupled with a microservices framework to maintain scalability and adaptability.Our methodology includes a thorough evaluation of various machine learning models (specifically focusing on R 2 , the coefficient of determination, as the metric) to ascertain their performance across different market scenarios.The results demonstrate the architecture's ability to handle high data volume and velocity, while accurately adapting to market changes.The adaptability is evidenced by the varying performance of models like Convolutional Neural Network (CNN), Gate Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) across different entities such as Royal Bank of Canada, Google, and EUR/USD, with the system successfully identifying the most suitable model in real-time.This architecture not only provides a scalable solution for stock market prediction but also sets the foundation for future exploration in other real-time data-intensive domains.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".