scholarly journals A continuous rainfall model based on vine copulas

2015 ◽  
Vol 12 (1) ◽  
pp. 489-524 ◽  
Author(s):  
H. Vernieuwe ◽  
S. Vandenberghe ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Time Series ◽  
Continuous Time ◽  
Rainfall Series ◽  
Two Dimensional ◽  
Statistical Comparison ◽  
Dimensional Dependence ◽  
Rainfall Model ◽  
Bivariate Dependence ◽  
Vine Copulas ◽  
Continuous Rainfall

Abstract. Copulas have already proven their flexibility in rainfall modelling. Yet, their use is generally restricted to the description of bivariate dependence. Recently, vine copulas have been introduced, allowing multi-dimensional dependence structures to be described on the basis of a stage by stage mixing of two-dimensional copulas. This paper explores the use of such vine copulas in order to incorporate all relevant dependencies between the storm variables of interest. On the basis of such fitted vine copulas, an external storm structure is modeled. An internal storm structure is superimposed based on Huff curves, such that a continuous time series of rainfall is generated. The performance of the rainfall model is evaluated through a statistical comparison between an ensemble of synthetical rainfall series and the observed rainfall series and through the comparison of the annual maxima.

scholarly journals A continuous rainfall model based on vine copulas

2015 ◽  
Vol 19 (6) ◽  
pp. 2685-2699 ◽  
Author(s):  
H. Vernieuwe ◽  
S. Vandenberghe ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Time Series ◽  
Continuous Time ◽  
Rainfall Series ◽  
Statistical Comparison ◽  
Model Based ◽  
Dimensional Dependence ◽  
Rainfall Model ◽  
Bivariate Dependence ◽  
Vine Copulas ◽  
Continuous Rainfall

Abstract. Copulas have already proven their flexibility in rainfall modelling. Yet, their use is generally restricted to the description of bivariate dependence. Recently, vine copulas have been introduced, allowing multi-dimensional dependence structures to be described on the basis of a stage by stage mixing of 2-dimensional copulas. This paper explores the use of such vine copulas in order to incorporate all relevant dependences between the storm variables of interest. On the basis of such fitted vine copulas, an external storm structure is modelled. An internal storm structure is superimposed based on Huff curves, such that a continuous time series of rainfall is generated. The performance of the rainfall model is evaluated through a statistical comparison between an ensemble of synthetical rainfall series and the observed rainfall series and through the comparison of the annual maxima.

Dynamic Modeling of Solar Radiation Disturbances Based on a Biomimetic Cloud Shading Model

2018 ◽  
Vol 140 (2) ◽  
Author(s):  
Jesús García ◽  
Iván Portnoy ◽  
Ricardo Vasquez Padilla ◽  
Marco E. Sanjuan
Keyword(s):  
Time Series ◽  
Solar Radiation ◽  
Control Strategies ◽  
Ground Level ◽  
Direct Solar Radiation ◽  
Complex Phenomenon ◽  
Two Dimensional ◽  
Multi Objective Optimization ◽  
Tuning Parameters ◽  
Radiation Time

Variation in direct solar radiation is one of the main disturbances that any solar system must handle to maintain efficiency at acceptable levels. As known, solar radiation profiles change due to earth's movements. Even though this change is not manipulable, its behavior is predictable. However, at ground level, direct solar radiation mainly varies due to the effect of clouds, which is a complex phenomenon not easily predictable. In this paper, dynamic solar radiation time series in a two-dimensional (2D) spatial domain are obtained using a biomimetic cloud-shading model. The model is tuned and compared against available measurement time series. The procedure uses an objective function based on statistical indexes that allow extracting the most important characteristics of an actual set of curves. Then, a multi-objective optimization algorithm finds the tuning parameters of the model that better fit data. The results showed that it is possible to obtain responses similar to real direct solar radiation transients using the biomimetic model, which is useful for other studies such as testing control strategies in solar thermal plants.

scholarly journals Some Applications of Lévy Processes to Stochastic Investment Models for Actuarial Use

1998 ◽  
Vol 28 (1) ◽  
pp. 77-93 ◽  
Author(s):  
Terence Chan
Keyword(s):  
Time Series ◽  
Brownian Motion ◽  
Differential Equations ◽  
White Noise ◽  
Stochastic Differential Equations ◽  
Continuous Time ◽  
Investment Model ◽  
Stable Processes ◽  
Gamma Processes ◽  
Investment Models

AbstractThis paper presents a continuous time version of a stochastic investment model originally due to Wilkie. The model is constructed via stochastic differential equations. Explicit distributions are obtained in the case where the SDEs are driven by Brownian motion, which is the continuous time analogue of the time series with white noise residuals considered by Wilkie. In addition, the cases where the driving “noise” are stable processes and Gamma processes are considered.

Approximations and boundary conditions for continuous-time threshold autoregressive processes

1994 ◽  
Vol 31 (4) ◽  
pp. 1103-1109 ◽  
Author(s):  
Rob J. Hyndman
Keyword(s):  
Time Series ◽  
Boundary Conditions ◽  
Continuous Time ◽  
Linear Time ◽  
Autoregressive Processes ◽  
Threshold Autoregressive ◽  
The Past ◽  
Time Threshold ◽  
Non Linear ◽  
The Way

Continuous-time threshold autoregressive (CTAR) processes have been developed in the past few years for modelling non-linear time series observed at irregular intervals. Several approximating processes are given here which are useful for simulation and inference. Each of the approximating processes implicitly defines conditions on the thresholds, thus providing greater understanding of the way in which boundary conditions arise.

scholarly journals A geographical location prediction method based on continuous time series Markov model

PLoS ONE ◽  
2018 ◽  
Vol 13 (11) ◽  
pp. e0207063 ◽  
Author(s):  
Yongping Du ◽  
Chencheng Wang ◽  
Yanlei Qiao ◽  
Dongyue Zhao ◽  
Wenyang Guo
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Continuous Time ◽  
Geographical Location ◽  
Prediction Method ◽  
Location Prediction
Sign in / Sign up

Export Citation Format

Share Document