Forecasting the Gross Domestic Product of the Philippines using Bayesian artificial neural network and autoregressive integrated moving average
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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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".