A Hybrid Ensemble Deep Learning and Reservoir Computing Approach for Exchange Rate Prediction
Bibliographic record
Abstract
A nation's currency plays a vital role in stabilizing the economy and determining exchange rates in global markets. Keeping track of the influential currencies while comparing them with one's own currency becomes essential these days. During this study, we have specifically focused on the exchange rate prediction between the United States dollar (USD) and the Canadian dollar (CAD). This pair is one of the most active currency pairs with significant economic implications for both nations. This paper studies the use of machine learning models for this specific matter, which includes long short-term memory (LSTM), gated recurrent units (GRU), and reservoir computing (RC) echo state network models. They were evaluated not only as individual models but also in various hybrid combinations. A hybrid model which combines LSTM and RC yielded better performance in terms of Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2). Moreover, the full potential of RC represents a promising direction for future researchers to incorporate into time series analysis. In addition, considering internal and external factors that influence the exchange rate during model development would also give more accurate predictions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".