scholarly journals An evolutionary Monte Carlo method for the analysis of turbidity high‐frequency time series through Markov switching autoregressive models

2021 ◽  
Author(s):  
Luigi Spezia ◽  
Andy Vinten ◽  
Roberta Paroli ◽  
Marc Stutter
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
High Frequency ◽  
Markov Switching ◽  
Autoregressive Models ◽  
Evolutionary Monte Carlo

scholarly journals Markov-switching autoregressive models for wind time series

2012 ◽  
Vol 30 ◽  
pp. 92-101 ◽  
Author(s):  
Pierre Ailliot ◽  
Valérie Monbet
Keyword(s):  
Time Series ◽  
Markov Switching ◽  
Autoregressive Models

Two-Particle Monte-Carlo Method for Warm Electrons in a High-Frequency Electric Field

1987 ◽  
Vol 142 (1) ◽  
pp. 247-254 ◽  
Author(s):  
Ž. Kancleris ◽  
A. Matulis
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Electric Field ◽  
High Frequency ◽  
High Frequency Electric Field ◽  
Frequency Electric Field

Online Bayesian Modeling and Prediction of Nonlinear Systems

2007 ◽  
Vol 46 (02) ◽  
pp. 96-101 ◽  
Author(s):  
T. Matsumoto
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Posterior Distribution ◽  
Sequential Monte Carlo ◽  
Time Series Data ◽  
Series Data ◽  
Target System ◽  
Sequential Monte Carlo Method ◽  
Online Prediction

Summary Objectives : Given time-series data from an unknown target system, one often wants to build a model for the system behind the data and make predictions. If the target system can be assumed to be linear, there are means of modeling and predicting the target system in question. If, however, one cannot assume the system is linear, various linear theories have natural limitations in terms of modeling and predictive capabilities. This paper attempts to construct a model from time-series data and make an online prediction when the linear assumption is not valid. Methods : The problem is formulated within a Bayesian framework implemented by the Sequential Monte Carlo method. Online Bayesian learning/prediction requires computation of a posterior distribution in a sequential manner as each datum arrives. The Sequential Monte Carlo method computes the importance weight in order to draw sample from the posterior distribution. The scheme is tested against time-series data from a noisy Rossler system. Results : The test time-series data is the x-coordinate of the trajectory generated by a noisy Roessler system. Attempts are made with regard to online reconstruction of the attractor and online prediction of the time-series data. Conclusions : The proposed algorithm appears to be functional. The algorithm should be tested against real world data.

Markov chain Monte Carlo method for the modeling of wind power time series

2012 ◽  
Author(s):  
Tong Wu ◽  
Xiaomeng Ai ◽  
Weixing Lin ◽  
Jinyu Wen ◽  
Luo Weihua
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Method ◽  
Wind Power

scholarly journals Detection of a sinusoidal oscillation of unknown frequency in a time series – a geodetic approach

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
R. Lehmann
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Hypothesis Test ◽  
Critical Values ◽  
Statistical Hypothesis ◽  
Ratio Test ◽  
Sinusoidal Oscillation ◽  
The Monte Carlo Method ◽  
Sinusoidal Oscillations

AbstractGeodetic and geophysical time series may contain sinusoidal oscillations of unknown angular frequency. Often it is required to decide if such sinusoidal oscillations are truly present in a given time series. Here we pose the decision problem as a statistical hypothesis test, an approach very popular in geodesy and other scientific disciplines. In the case of unknown angular frequencies such a test has not yet been proposed.We restrict ourselves to the detection of a single sinusoidal oscillation in a one-dimensional time series, sampled at non-uniform time intervals.We compare two solution methods: the likelihood ratio test for parameters in a Gauss-Markov model and the analysis of the Lomb-Scargle periodogram. Whenever needed, critical values of these tests are computed using the Monte Carlo method. We analyze an exemplary time series from an absolute gravimetric observation by various tests. Finally, we compare their statistical power. It is found that the results for the exemplary time series are comparable. The LR test is more flexible, but always requires the Monte Carlo method for the computation of critical values. The periodogram analysis is computationally faster, because critical values can be approximately deduced from the exponential distribution, at least if the sampling is nearly uniform.

The estimation techniques of the time series correlations in nuclear reactor calculations by the Monte Carlo method using multiprocessor computers

2012 ◽  
Vol 75 (14) ◽  
pp. 1669-1674 ◽  
Author(s):  
M. A. Kalugin ◽  
D. S. Oleynik ◽  
E. A. Sukhino-Khomenko
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Nuclear Reactor ◽  
Estimation Techniques ◽  
The Monte Carlo Method

Markov chain Monte Carlo method in Bayesian reconstruction of dynamical systems from noisy chaotic time series

2008 ◽  
Vol 77 (6) ◽  
Author(s):  
E. M. Loskutov ◽  
Ya. I. Molkov ◽  
D. N. Mukhin ◽  
A. M. Feigin
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Method ◽  
Dynamical Systems ◽  
Chaotic Time Series ◽  
Bayesian Reconstruction

scholarly journals Uncertainty assessment

2021 ◽  
pp. 263-326
Author(s):  
Jean-Luc Bertrand-Krajewski ◽  
Mathias Uhl ◽  
Francois H. L. R. Clemens-Meyer
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Stormwater Management ◽  
Uncertainty Assessment ◽  
Repeated Measurements ◽  
Urban Drainage ◽  
Standard Methods ◽  
Advanced Method ◽  
Propagation Of Uncertainties

Abstract Assessing uncertainties in measurements must become a standard practice in the field of urban drainage and stormwater management. This chapter presents three standard methods to estimate uncertainties: the Type A method (repeated measurements), the Type B method (law of propagation of uncertainties) and the MC method (Monte Carlo method). Each method is described with its fundamental principles and equations, various examples are presented in detail and Matlab® codes are given to facilitate the calculations for routine applications. An advanced method to account for partial autocorrelation in time series is presented. Lastly, typical orders of magnitude of standard uncertainties for usual sensors used in urban drainage and stormwater management are given.

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