Research on Stock Price Prediction Based on Orthogonal Gaussian Basis Function Expansion and Pearson Correlation Coefficient Weighted LSTM Neural Network
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
For stock price prediction in quantitative finance, deep learning techniques such as LSTM neural network do not need the stationarity assumption of traditional time series models (such as ARIMA and GARCH) and can forecast medium and long-term time series, so they have attracted much attention. This paper proposes an improved LSTM neural network based on orthogonal Gaussian basis function expansion and Pearson correlation coefficient weighting. The proposed method uses the functional features of intra-day prices to fit the residual series predicted by the LSTM neural network. Considering that the underlying model structure between each component of the function eigenvector and the residual series is unknown, we use the Bagging method to capture and trade off the variance and bias of the prediction model. In addition, since the dimension of the predictive variable of the LSTM neural network is a parameter to be estimated, we use the model averaging method based on Pearson correlation coefficient weighting for tuning. The results of actual data analysis show that the proposed method can significantly improve the prediction accuracy of the original LSTM neural network and has certain robustness. Finally, the proposed method can be further applied to consumer price index (CPI) prediction, daily average temperature prediction, and real-time monitoring of environmental trace elements.
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 | 0.014 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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