Time series prediction of novel coronavirus COVID-19 data in west Java using Gaussian processes and least median squared linear regression
Bibliographic record
Abstract
In 2019, the COVID-19 epidemic swept throughout the globe. The virus was first identified in Wuhan, China. By the time several months had gone by, this virus had spread to numerous locations throughout the world. Consequently, this virus has become a worldwide pandemic. Multiple efforts have been made to limit the transmission of this virus. A possible course of action is to lock down the territory. Unfortunately, this strategy wrecked the economy, worsening the terrible situation. The world health organization (WHO) would breathe a sigh of relief if there were to be no new cases. However, the government should explore employing data from the future in addition to the data it already has. Prediction of time series may be utilized for this purpose. This study indicated that the Gaussian processes method outperformed the least median squared linear regression method (LMSLR). Applying a Pearson VII-based global kernel produces MAE and RMSE values of 23.12 and 53.43, respectively.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".