A sequential learning neural network for foreign exchange rate forecasting
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 paper, a sequential learning neural network, named as minimal resource allocating network (MRAN), is used to forecast monthly exchange rates between the U.S. dollar and the Deutsche mark, the British pound and the Canadian dollar. Five dominant economic structural exchange rate models are employed as the inputs of MRAN. Although the neural network cannot beat the simple random walk model without drift in out-of-sample forecast accuracy, it is better than the multilayer perceptron (MLP) neural network and the random walk model with drift in trend forecasting. The phenomena that the preferable structure of exchange rate model varies in different short periods are discovered from the simulation results. A simple model-competition methodology, purposing to choose the dominant model for next forecasting from the candidate models according to their previous short-term performance, is tested and found to improve the forecasting performance in forecast accuracy and direction accuracy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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