Forecasting the stock price time series via all components of multi resolution
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
Objectives: This research study presents a forecasting model that integrates an efficient discrete wavelet transform and a Backpropagation Neural Network (BPNN) for predicting financial time series. Methods/Statistical analysis: The presented model uses the wavelet transform at several time instances based on local smooth B-Spline wavelets of order d(BSd) to decompose the financial time series data. So, an approximation (long-term trends) component and several details (shortterm deviations) components are obtained. Since the details components act as a complementary part of the approximation component, to prepare a prediction model which applies all decomposed components is very advantageous. Therefore, all components are used as smooth input samples of the neural network to forecast the future of the financial time series. Findings: The proposed model is designed to forecast the stock prices of five different companies, and according to the obtained results, the presented model outperforms a conventional model that uses only the approximation component as a wavelet de-noising-based model. The numerical results have shown the prediction accuracy. Applications/Improvements: The proposed model can predict future stock prices better than the de-noised based model in nearly 70%cases. Keywords: B-Spline Wavelets Multiresolution, Back Propagation Neural Network, Financial Time Series, Stock Market Prediction
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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