ADAPTEN: Adaptive Ensembles Leveraging Feature Engineering for Real-Time Market Analysis
Bibliographic record
Abstract
In an era of significant economic volatility, time series forecasting is widely used to predict stock prices and guide investors in trading decisions. Nevertheless, existing data-driven techniques are unable to effectively handle the vast amount of financial data due to big data constraints such as nonlinearity, non-stationarity, heteroskedasticity, and unsynchronicity. A cohesive framework is also required for ensuring the smooth integration and synchronization of varied methodologies in timeseries financial prediction tasks. To address this problem, this paper introduces a novel framework that investigates three ensemble strategies: blending, stacking, and voting, and selects the best method to perform the stock trend prediction task. Specifically, we deploy four distinct machine learning algorithms as the base learning model, each of which is uncorrelated and proficient in a different way depending on the task. The outputs of the basis classifiers are then combined using the adaptive boosting algorithm, a meta classifier, to give the final prediction results. To augment predictive models's accuracy and generalization capabilities, we put forward strategies like feature engineering and Ridge regularization, which optimize the pertinence of data and curb overfitting. Our examination of five distinct case studies on Toronto Stock Exchange data reveals that the proposed multimodel ensemble method has superior performance compared to others.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".