Forecasting the Gross Domestic Product of the Philippines 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
The researcher aim to forecast the Gross Domestic Product (GDP) of the Philippines from the 1st Quarter of 2018 to 4th Quarter of 2022. Furthermore, this study determines the most suitable model among Autoregressive Integrated Moving Average and Bayesian Artificial Neural Network that can forecast the GDP of the Philippines. The researcher used the data ranging from the 1st Quarter of 1990 up to 4th Quarter of 2017 with a total of 112 observations. Statistical test are conducted within the study to be able to formulate and compare the statistical model ARIMA and Bayesian ANN. It is concluded in this study that the ARIMA(1,1,1) and Bayesian ANN can forecast the GDP of the Philippines. The researcher use Forecasting accuracy such as MSE, NMSE, MAE, RMSE, and MAPE to compare the performance of two models. In this paper, the best fitted model obtained is Bayesian ANN. Paired T-test concludes that there is no significant difference between actual and predicted value. This study helps economics specifically in economic forecasting and economic analysis.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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