The Analysis to Tertiary-industry with ARIMAX Model
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 application of multivariate time series is so large,it can be used in many systems, like ecnomic systems,biologicalsystems, and so on.This paper introduced the method’s building and the structure of ARIMAX model (auto-regressiveintegrated moving average model with explanatory variables) and its SAS realizing. The paper analysed the tertiaryindustryin China with the realty business to be input variable and proved that there had been co-integration relationshipbetween the two time serieses. Then, the paper modeled an appropriate ARIMAX model to tertiary-industry and fitthis model with the real statistics(the tertiary-industry’s production values in China from 1978 to 2007). And the resultshowed that ARIMAX, applied ARIMAX model to analyzing and forecasting of tertiary-industry, it is a model with highprediction precision.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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