Forecasting Apple Stock Closed Prices by LR and LSTM with Discrete Wavelet Transformation
Bibliographic record
Abstract
Stock prediction has long had a high profile among investors under the incentives of profit maximization.However, as a result of the instability and chaos of the financial stock market, predicting stock prices is challenging.To address this problem, the discrete wavelet transformation (DWT) is applied to denoise stock prices when data preprocessing.Long short-term memory (LSTM) and linear regression model (LR) are chosen to train the model.The performances of LR, LSTM, the combination of DWT and LR and the combination of DWT and LSTM are demonstrated and compared when predicting the Apple stock closed prices by using its rescaled closed price five days ago.The prediction results proved the effectiveness of DWT and illustrated LR still acts well although it is much simpler compared with LSTM in terms of RMSE, MAE, MAPE.These model-based analytic strategies and pre-programmed stock price prediction are likely to give precious guidance to investors in the pursuit of maximum benefits.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".