Long-Term Forecasting of Euro-Dollar Exchange Rates Using the ARIMA Model and Multilayer Perceptron
Bibliographic record
Abstract
Forecasting exchange rates is a complex problem due to the inherent volatility and complex dynamics of exchange rates.Traditional forecasting models such as ARIMA often cannot capture these complexities especially for long-term forecasts.The objective of this study is to develop an accurate forecasting model for long-term exchange rates.A data set of eurodollar exchange rates from 2017 to 2022 was used for the present analysis.ARIMA and MLP models were developed and their performances were compared; the optimized MLP model equipped with 11 input neurons derived from significant lags achieved a scaled mean absolute error (MASE) of 0.75 on the test data while the MLP model significantly outperformed the ARIMA model, demonstrating its ability to capture underlying patterns and trends in the exchange rate data.The optimized MLP model also provided a 365-day forecast for 2023 exchange rates.The results of this study suggest that MLP models are a promising tool for long-term forecasting of exchange rates.Their ability to capture complex nonlinear relationships and adapt to changing market conditions makes them well suited for this challenging task.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".