Results 41 to 50 of about 493 (178)
Moving Aggregate Modified Autoregressive Copula‐Based Time Series Models (MAGMAR‐Copulas)
Journal of Time Series Analysis, EarlyView.ABSTRACT Copula‐based time series models can model univariate and stationary time series in a flexible way by decomposing the joint distribution of consecutive observations into a copula and the stationary distribution. Implicitly, this approach assumes a finite Markov order. In reality, a time series may not follow the Markov property.
Sven Pappert
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A Vine-copula extension for the HAR model [PDF]
, 2019 24 pages, 3 figures, 3 ...
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Bayesian Model Selection of Regular Vine Copulas [PDF]
Bayesian Analysis, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gruber, Lutz F., Czado, Claudia
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IEEE AccessUsing copulas in statistics evaluates the dependence between random variables. Copula modeling has significantly been used in many areas, especially in the search for multivariate distributions.
Amir Shahirinia +4 more
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Time series with infinite-order partial copula dependence
Dependence Modeling, 2022 Stationary and ergodic time series can be constructed using an s-vine decomposition based on sets of bivariate copula functions. The extension of such processes to infinite copula sequences is considered and shown to yield a rich class of models that ...
Bladt Martin, McNeil Alexander J.
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The Canadian Journal of Chemical Engineering, Volume 104, Issue 8, Page 4079-4100, August 2026.Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos +3 more
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Bivariate postprocessing of wind vectors
Quarterly Journal of the Royal Meteorological Society, Volume 152, Issue 778, July 2026 Part A.We introduce three novel bivariate postprocessing approaches and analyze their performance for joint postprocessing of bivariate wind‐vector components in Germany. Bivariate vine‐copula‐based models, a bivariate gradient‐boosted version of ensemble model output statistics (EMOS), and a bivariate distributional regression network (DRN) are compared with
Ferdinand Buchner +3 more
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Vine Copulas as Differentiable Computational Graphs
CoRRVine copulas are sophisticated models for multivariate distributions and are increasingly used in machine learning. To facilitate their integration into modern ML pipelines, we introduce the vine computational graph, a DAG that abstracts the multilevel vine structure and associated computations.
Tuoyuan Cheng +3 more
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Applied Water Science, 2023 Estimation of instantaneous peak discharge is important in the design of hydraulic structures and reservoir management. In this study, a new approach called CEEMD-Copula-GARCH is presented for simulating instantaneous peak discharge in the Qale Shahrokh ...
Mohammad Nazeri Tahroudi +1 more
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Risk return of forward contracting corn with crop insurance
Journal of the Agricultural and Applied Economics Association, Volume 5, Issue 2, Page 139-153, June 2026.Abstract Forward contracting is a common pre‐harvest marketing strategy for row crops, with evidence suggesting higher prices during summer months due to embedded weather risk premiums. While aggressive forward contracting increases farmers' yield risk and potential non‐delivery penalties, crop revenue protection can help offset these financial burdens.
Chandan Bhattarai +4 more
wiley +1 more source