Forecasting occurrence and quantity of monthly precipitation simultaneously while accounting for complex serial correlation
Bibliographic record
Abstract
Abstract Tweedie's compound Poisson regression models have been introduced in recent years to model and predict daily, monthly and seasonal precipitation data. Tweedie's compound Poisson regression analysis of precipitation data captures the relationship between the mean structure of precipitation and covariates while accounting for its right‐skewness and zero‐inflation appropriately, and thus extends trend analysis stage of classical time series techniques to handle right‐skewed time series data with possible zero‐inflation. A distinctive advantage of Tweedie's compound Poisson modelling of precipitation data is that the occurrence and quantity of precipitation can be simultaneously modelled using a single distribution; however, this approach ignores serial correlation between precipitation data observed over time. In this study, we propose to accommodate complex correlation structures of time series precipitation data using the Autoregressive Integrated Moving Average (ARIMA) models at the next stage as done in the classical time series analysis. Our analyses of monthly precipitation data in Australia demonstrated the usefulness of our two‐stage approach to prediction of precipitation.
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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 | 0.000 | 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 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".