Long Short Time Memory in the forecast of financial indices in the Brazilian market (Ibovespa)
Bibliographic record
Abstract
The present investigation seeks to evaluate different models of recurrent neural networks such as Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BLSTM) and Gated Recurrent Units (GRU) in comparison with the ARIMA model, whose purpose is to find which of these models is capable of making a better forecast for the closing price of 5 steps forward in the stock index of the Sao Paulo Stock Index (IBOVESPA).The optimization of the parameters makes it possible to reduce the cost function (minimum square error).Based on 8 configurations with more than 720 simulations, we discovered that the ADAMAX optimizer has performed better compared to the NADAM and ADAM optimizers, presenting a lower cost function.In the simulations of the different configurations, the average and the standard deviation of different models have been considered.The GRU model with the ADAMAX optimizer was more efficient in more than 90% of the results obtained.The final configuration was the GRU model with a batch size equal to 5, with 250 epochs, a learning ratio equal to 0.001 and with 30 neurons.This configuration presented a lower mean square error and therefore better forecasts.Therefore, the LSTM and BLSTM models did not present a lower cost function compared to the GRU model.Also, the ARIMA model did not have an optimal result compared to recurrent neural network models..
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".