DATA FORECASTING ANALYSIS OF GROSS REGIONAL DOMESTIC PRODUCT (PDRB) AS A REJECT MEASURE OF ECONOMIC PERFORMANCE OF BANGKA BELITUNG ISLANDS PROVINCE
Bibliographic record
Abstract
Gross Regional Domestic Product (GDP) is the total number of products in the form of goods and services produced by production units within the boundaries of a country (domestic) for one year which is one important indicator to know the condition of an area within a certain period either at current or at constant prices. This research briefly wants to know the value or prediction (forecasting) from data of Gross Regional Domestic Product (PDRB) of Bangka Belitung Islands Province for the preventing action related to policy type which will be done by the decision maker. The method of forecasting used in this research is ARIMA method with the utilization of software R in the form of the value of significant t-test and by using parsimony principle consisting of two methods combined into one, namely AR (Autoregressive) and MA (Moving Average). This ARIMA model in its application is often written with ARIMA (p, d, q) whose descriptions p, d, q are the same as the previous ones. This method uses an iterative approach to the identification of an existing model. It can be seen that historical data have forecasting results that tend to have an uptrend as evidenced by the Q Ljung-Box test and ACF / PACF plot of data with the assay results that the residuals of the ARIMA model (1,1,0) are good models evidenced by plots ACF that there is no lag out of the interval line with the forecasting result that has the trend up from the third quarter of 2013 ie 11.028.917, 11.223.615, 11.360.718, 11.456.826, 11.523.985 and 11.570. 813. The results of this forecasting can certainly be applied for n years to come.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".