Research on Stock Price Prediction Based on CNN-LSTM Combined Model
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
It is difficult to forecast time series data such as stock prices because of the complexity of the internal structure of the stock price system and the diversity of external factors that determine the complexity of the stock market and the uncertainty of the stock price forecasting task. In addition, the impact of different factors on stock prices may be linear or non-linear. Therefore, this paper proposes a combined CNN-LSTM model with good results and generalization ability, which can fully exploit the advantages of each model in stock price prediction and improve the accuracy and stability of prediction. This research can improve the effectiveness of stock price prediction by constructing suitable model structures, parameter configurations and optimization algorithms to provide investors and financial practitioners with a powerful reference for decision-making. An attention mechanism is also introduced in order to improve the prediction accuracy of the CNN-LSTM model, and the results confirm the efficacy of the proposed approach.
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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 | 0.019 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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