Forecasting Philippines imports and exports using Bayesian artificial neural network and autoregressive integrated moving average
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 research, Autoregressive Integrated Moving Average (ARIMA) and Bayesian Artificial Neural Network (BANN) were used in forecasting the imports and exports of the Philippines and the comparison of two models are one of the main objective of this research. The data were gathered from Philippines Statistical Authority with a total of 100 observations from the first quarter of 1993 to fourth quarter of 2017. Furthermore, it can be determined in this research the best fit among the models in forecasting the imports and exports of the Philippines and the researchers will give the forecast values of imports and exports from the first quarter of year 2018 to the fourth quarter of year 2022 using the most fitted model. The researchers conducted a Statistical test in order to formulate and compare the statistical models of ARIMA and BANN for imports and exports then applied the forecasting accuracy such as MSE, NMSE, MAE, RMSE, and MAPE to compare the performance of the two models. By comparing the results, the researchers concluded that Bayesian Artificial Neural Network is the most fitted model in forecasting the imports and export of the Philippines. Upon using the Paired T-test, the p-value for both imports and exports are greater than the level of significance (α = 0.01) which means that there is no significant difference between actual and predicted values for both imports and exports of the Philippines. This study could help the economy of the Philippines by considering the forecasted Imports and Exports which can be used in analyzing the economy’s trade deficit.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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