Optimizing LSTM Based Network For Forecasting Stock Market
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 this modern era, the financial market, more specifically, the stock markets all over the world, deal with an enormous amount of real-time data that facilitates the data analytics and prediction in the field of finance. The main objective of this paper is to propose a novel model of neural network based on Long-Short Term Memory (LSTM) and utilizing one of the most powerful evolutionary algorithms, namely the Differential Evolution (DE), to forecast the next day's stock price of a company. This study focuses on optimizing the ten network hyperparameters related to the detection of temporal patterns of a given dataset, namely, the size of the time window, batch size, the number of LSTM units in hidden layers, the number of hidden layers (LSTM and dense), dropout coefficient for each layer, and the network training optimization algorithm. To the best of our knowledge, this is the first time that all this set of parameters have been optimized simultaneously. Then, the LSTM has been optimized by DE to gain the lower root mean squared error (RMSE) for prediction. The proposed model achieved 8.092 RMSE as its objective value, which is better in comparison with the best statistical forecasting models such as NAIVE, ETS, and SARIMA, which are the-state-of-the-art methods in this filed. Moreover, for shortening the training time as the main source of computational expensiveness, the proposed method works with a lower number of epochs. By this way, DE tries to find a shallower and faster network even with higher accuracy, which is a remarkable approach.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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