A Stock Price Prediction Model Based on MLP
Bibliographic record
Abstract
This paper proposes a stock price prediction model based on a multilayer perceptron (MLP) network, using NVDA (NVIDIA Corporation) stock as the research subject. First, the fundamental principles and structure of the MLP network are introduced, along with its advantages in regression tasks, particularly its ability to handle complex nonlinear relationships. Then, historical data of NVDA stock is preprocessed using Python and relevant libraries (such as Pandas, NumPy, Matplotlib, and PyTorch), including data normalization and the division of training and testing sets. This paper provides a descriptive summary of the construction of the MLP model and its parameter setup and optimization. In training, the loss function employed is mean squared error (MSE), and the parameter update rule is the Adam optimizer. Experiments are conducted to evaluate the model's efficiency by analyzing its prediction, and the actual vs. predicted prices are represented visually. The MLP model demonstrates effectiveness in NVDA stock price forecasting tests where it delivers precise price fluctuation pattern recognition. This paper investigates the model prediction accuracy and enhancement potential together with financial market prospects for the MLP model's stock price prediction capabilities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".