BiMT-TCN: A cutting-edge hybrid model for enhanced stock price prediction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the face of the rapid evolution and escalating complexity of financial markets, precise stock price prediction has become a critical area of research for scholars and practitioners alike. Stock markets are subject to a vast array of influencing factors, both internal and external, which complicates prediction efforts. This study proposes BiMT-TCN, a novel model combining Bidirectional Long Short-Term Memory (BiLSTM), a modified Transformer, and Temporal Convolutional Network (TCN), aimed at enhancing the accuracy and stability in stock price prediction. BiLSTM facilitates the capture of bidirectional dependencies, which aids in decoding the intricate patterns within time-series data. The modified Transformer integrates global information, enhancing the model’s capacity to manage long-range dependencies effectively. TCN, known for its parallel processing and proficiency in capturing deep historical patterns, further bolsters model stability and generalizability. Empirical evaluations on major indices such as SSE, HSI, and NASDAQ demonstrate that BiMT-TCN consistently outperforms state-of-the-art models, achieving R 2 scores of 0.9779, 0.9776, and 0.9969 respectively, along with significantly lower RMSE, MAE, and MAPE values. The implications of this work extend to practical investment decision-making, where improved forecast precision can enhance risk management, optimize trading strategies, and inform financial planning in volatile markets.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it