Time Series Forecasting for Regional Development Composite Index Using Real-Time Floating Population Data
Bibliographic record
Abstract
Composite development indices show an exponential movement of major economic indicators to identify and predict the overall trend of the national economy. However, the existing method of writing composite development indices is based on simple statistical methods using macroscopic data. Therefore, it presents limitations when grasping regional economic trends late. It is because the time of announcement of composite development indices is concentrated at the end of each month, quarter, and year. This study used the floating population estimated from smartphone data that can be collected in real-time to analyze how floating population patterns affect regional economic situations to compensate for these limitations. The primary purpose was to present a prompt development prediction methodology that reflects this meaningful relationship. A correlation and cross-correlation analysis was performed to exhibit a clear relationship between composite development indices and floating population value. In addition, a time series model and a multiple regression model analyses were applied to predict regional development indices. The results obtained facilitated the prompt selection of regional composite indices after choosing a model that exhibits high prediction accuracy and efficiency of the application. The selected regional development composite indices are expected to be used as a faster and more reliable prediction criterion than the existing development composite indices used to predict a specific city’s economic situation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".