Application of ARIMA Model in Forecasting Exchange Rate: Evidence from Bangladesh
Why this work is in the frame
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Bibliographic record
Abstract
This paper attempts to apply the ARIMA time series model to forecast the exchange rate of seven currencies (United States Dollar, Euro, Pound sterling, Australian Dollar, Japanese Yen, Canadian Dollar and Swedish Krona) in terms of Bangladeshi Taka (BDT) and to investigate the accuracy of the model by comparing the forecasted rates with the actual exchange rates. It considered daily currency exchange rates (244 selling price) of seven currencies for twelve months from January 2018 to December 2018 to forecast the subsequent one month (25 selling rate) in January 2019 rate. The Durbin-Watson test result shows an autocorrelation in the daily foreign currency exchange rate with the previous rate. The Augmented Dickey-Fuller test result shows data have unit roots and non-stationary. But the 1st differencing becomes data stationary to apply d equal to 1 in ARIMA model. Also, autocorrelation function considers MA(0) and partial autocorrelation function considers AR(1) for the ARIMA model. So, ARIMA (1,1,0) models are selected based on Ljung-Box test, root mean square error, mean absolute percent error, mean absolute error and R-square values. By using the above ARIMA models, forecasted foreign currency exchange rates next one month calculated and compared with the respective actual rates, which validate with Chi-Square test, mean absolute percent error, mean square error, root mean square error values of Goodness fit test. The result shows that predicted foreign currency exchange rates follow ARIMA (1,1,0) model, which may be applied to forecast the foreign currency exchange rates in Bangladesh.
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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 | 0.039 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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