Clustering wildfire occurrence time series
Bibliographic record
Abstract
Exponential smoothing methods offer several tools for forecasting and simulating varying time series patterns, including historical wildfire counts. The Box-Cox transform, ARMA errors, trend and seasonal components (BATS), and the trigonometric BATS model (TBATS) are exponential smoothing techniques capable of modeling complex seasonality, non-integer frequencies, and more. The BATS and TBATS frameworks support short- and intermediate-term forecasting and simulation, while also providing a foundation for model-based time series clustering of wildfire counts. The application in this thesis of the BATS and TBATS models aims to support fire agencies in forecasting and simulating wildfire occurrences across Canada on an increased scale that provides alternative methods for future use. Variable length Markov chains (VLMCs) are an extension of traditional fixed-order Markov chains that can reduce model complexity and improve computational efficiency. To assess the practicality and effectiveness of VLMCs in modeling wildfire causes in Canada, they are evaluated in terms of forecasting on both balanced and unbalanced data. Furthermore, VLMCs are used to model the cluster assignment results on the weekly wildfire count time series data over a range of years. We provide preliminary information on clustering weekly wildfire counts using a model-based approach built from TBATS models that can help identify broad characteristics for an upcoming fire season through extensions of Markov chains.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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".