Mitigating Risks: A Hybrid Autoregressive Integrated Moving Average-Artificial Neural Network (ARIMA-ANN) Methodology for Exchange Rate Volatility
Bibliographic record
Abstract
This study aims to estimate the rupiah exchange rate against the US dollar by employing a hybrid ARIMA-Artificial Neural Network (ARIMA-ANN) methodology, with export treated as an exogenous variable. It evaluates the precision of the model against a non-hybrid model. Multiple types of research have demonstrated the efficacy of the hybrid ARIMA-ANN model in minimizing errors, thereby justifying its selection. The hybrid ARIMA-ANN methodology employs ANN to discern nonlinear patterns in time series data and ARIMA to detect linear patterns. The results of this research indicate that the hybrid ARIMA-ANN model yields more precise forecasts. The RMSE value of 0.025 contrasts with the RMSE of 0.045 for the ARIMA model and 0.035 for the ANN. The significance of projecting exchange rate volatility holds both practical and scholarly value. Our study offers new insight by thoroughly analyzing the predictive capacity of financial and macroeconomic variables related to future exchange rate volatility.
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How this classification was reachedexpand
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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".