Semiparametric and parametric distributional forecasting of univariate time series using non‐Gaussian ARMA models based on D‐vines
Bibliographic record
Abstract
Abstract A methodology for modelling and forecasting univariate time series using non‐Gaussian ARMA and seasonal ARIMA models based on D‐vine copulas is proposed. By combining a parametric D‐vine process to describe serial dependence with a nonparametric or parametric model of the marginal distribution, the method offers improved modelling and distributional forecasting for time series that have a non‐Gaussian distribution and a nonlinear dependence on past values. While D‐vine copula‐based models of univariate time series are known to generalize the classical Gaussian autoregressive (AR) model, an innovative method of parametrization based on the Kendall partial autocorrelation function is shown to permit models that generalize any ARMA model. Simulations and examples of real data show the forecasting advantages of using non‐Gaussian and nonlinear serial dependence structures, as well as the advantages of improved marginal modelling that are offered by a copula approach.
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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 | 0.001 |
| 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".