Forecasting Stock Prices using Gated Recurrent Unit with the Help of Feature Engineering
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
Forecasting the stock prices is one of the hardest tasks because of its volatility and uncertainty, where additional features such as foreign exchange impacts the stock markets. Introducing AI in forecasting has helped in prediction tasks, especially the time series algorithms. This study attempts to implement various regression models such as the ARIMA model, which gives us improved results for data of linear nature but it suffers from conditions such as non-linearity, unable to cope up with dynamic changes in prices, and overdependence on historical data. This study attempts to implement ML and DL mechanisms to predict stock prices. As most stock markets deal with probabilistic functions and mathematical computations, usage of technologies such include algorithms for machine learning and deep learning. The proposed objective is to forecast the stock prices to enable the users to make decisions and undertake profitable trades within certain intervals by using GRU (Gated Recurrent Unit).
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| 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