Proceedings ArticleDOI
Modeling and generating multivariate time series with arbitrary marginals and autocorrelation structures
Bahar Deler,Barry L. Nelson +1 more
- Vol. 1, pp 275-282
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TLDR
The authors present a general-purpose input-modeling tool for representing, fitting, and generating random variates from multivariate input processes to drive computer simulations.Abstract:
Providing accurate and automated input modeling support is one of the challenging problems in the application of computer simulation. The authors present a general-purpose input-modeling tool for representing, fitting, and generating random variates from multivariate input processes to drive computer simulations. We explain the theory underlying the suggested data fitting and data generation techniques, and demonstrate that our framework fits models accurately to both univariate and multivariate input processes.read more
Citations
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Joint probability analysis for estimation of extremes
TL;DR: This work describes the development and testing of methods for incorporation of temporal dependence into an approach involving Monte Carlo simulation, and summarises the terminology and the types of method available for joint probability analysis.
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Designing a multivariate–multistage quality control system using artificial neural networks
TL;DR: In this article, a single neural network is designed, trained and employed to control and classify mean shifts in quality characteristics of all stages of a multivariate-multistage manufacturing process.
Journal ArticleDOI
Wind speed modeling using a vector autoregressive process with a time-dependent intercept term
Matti Koivisto,Janne Seppanen,Ilkka Mellin,Jussi Ekström,John Millar,Ivan Mammarella,Mika Komppula,Matti Lehtonen +7 more
TL;DR: In this paper, a vector-Autoregressive-to-anything (VARTA) process with a time-dependent intercept is presented to model wind speeds in multiple locations.
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A statistical model for comparing future wind power scenarios with varying geographical distribution of installed generation capacity
TL;DR: In this article, the authors introduce a statistical model that can be used to estimate the variability in wind generation and assess the risk of wind generation contingencies over a large geographical area.
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Statistical simulation of flood variables: incorporating short‐term sequencing
TL;DR: In this paper, a general method for simulating univariate time series data, with a given marginal extreme value distribution and required autocorrelation structure, together with a demonstration of the method with synthetic data, is presented.
References
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Book
Introduction to multiple time series analysis
TL;DR: The choice of point and interval forecasts as well as innovation accounting are presented as tools for structural analysis within the multiple time series context.