Research on financial time series prediction based on deep learning
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 contrast to conventional machine learning approaches, deep neural networks excel at extracting relevant features and adeptly identifying complex, nonlinear interactions within datasets. This attribute renders them particularly advantageous for the task of forecasting financial time series data. This article selects the five-dimensional indicators of the Shanghai Composite Industrial Index from April 17, 2020 to April 15, 2022, including closing price, opening price, highest price, lowest price, and trading volume, and conducts empirical mode decomposition on these five-dimensional indicators. This article uses the CNN-LSTM model to predict financial time series and obtain images of true and predicted values. The results show that the CNN-LSTM model proposed in this article has smaller prediction errors and improves prediction performance.
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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 | 0.021 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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