Stock price prediction based on SVM, LSTM, ARIMA
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 general, forecasting on stock prices is a famous and interesting area that gathers many researchers in. Contemporarily, after the birth of AI, the number of the algorithms used in the prediction of equity market fluctuation are boomed rapidly. Applying the combination of statistics and algorithms can help researchers as well as investors learn about either short-term regulation (such as opening price) or the long-term market movement. This paper discusses three kinds of models which are used to predict the stock price for long or short term. Specifically, some empirical results are presented to prove the feasibility and significance of the models. By analyzing techniques used to predict stock prices and the limitation of these models, the discussion about the challenges and the outlook posed from the scope of future work in this filed are also shown and demonstrated. These results shed light on guiding further exploration of price forecasting for different kinds of underlying assets as well as portfolios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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