Bibliographic record
Abstract
The objective of this study is to utilize a combined model of two algorithms, namely Long Short-Term Memory network and Recurrent Neural Network, to forecast the stock price of Google. Using Google stock price data from 2010 to 2022 as the training set and performed data preprocessing and feature engineering. This then build a deep neural network model consisting of multiple LSTM and RNN layers and train it by the backpropagation algorithm. During training, this paper employs an appropriate loss function and optimizer to minimize the prediction error. In conclusion, the performance of the model was assessed by employing Google stock price data from 2023 as a test set. By comparing the error between the actual stock price and the predicted value of the model, it can evaluate the accuracy and stability of the model. The experimental results show that the superposition model using LSTM and RNN algorithms can effectively predict the Google stock price with high accuracy and stability. This research presents a practical approach that can enhance the predictive capabilities of investors, financial institutions, and other related domains, enabling them to make well-informed investment decisions in the stock market.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".