Forecasting natural disaster frequencies using nonstationary count time series models
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
Because of the climate change, the frequency of natural disaster might be evolving. This could mean an increasing expected risk, and/or more and more uncertainties. In this paper, we identify three potentially suitable models, that are the nonstationary INGARCH(1, 1), the nonstationary INAR(p), and the state-space model of Harvey and Fernandes (J Bus Econ Stat 7(4):407–417, 1989). We derive properties of their long-run behavior, discuss their link and differences, and assess their suitability for Canadian climate event data. We show that first, the Harvey–Fernandes model or INGARCH(1, 1) model often provides better fit and better prediction performance. Second, the long-run prediction of these models can differ substantially, highlighting model uncertainty.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it