Predictive Analysis Based on Prophet Model: Evidence from the Number of Epidemic Infections
Bibliographic record
Abstract
This study makes a prediction of infection numbers in the future and investigates the correlation between the new cases and the stock price of companies in three different fields. In the first part of the prediction, we use three methods to make predictions of the future infected number, including the ETS, Auto-Regressive Moving Average (ARIMA), and Prophet model. In the next part, we use the GARCH model to discuss the correlation between infection numbers and stock prices. Considering the heterogeneity, we choose three different companies’ stocks to represent three industries respectively. They are Apple, Amazon and Pfizer. The result indicates that the stock price of Apple is negatively correlated to the infection number, the relationship between the infection number and the price of Amazon is inconspicuous, while the price of Pfizer is positively correlated with the development of the pandemic. Thus, we draw the conclusion that the impact of the epidemic is strong to manufacturing, is not obvious to the Internet industry, and boosts the development of the medical corporation.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".