scholarly journals Learning Dynamics in a Nonlinear Stochastic Model of Exchange Rates

1996 ◽  
Author(s):  
Carl Chiarella ◽  
Alexander Khomin
Keyword(s):  
Stochastic Model ◽  
Exchange Rates ◽  
Learning Dynamics ◽  
Nonlinear Stochastic Model

Nonlinear Stochastic Model of Rainfall Runoff Process

1984 ◽  
Vol 20 (2) ◽  
pp. 297-309 ◽  
Author(s):  
Srinivas G. Rao ◽  
Ramachandra A. Rao
Keyword(s):  
Stochastic Model ◽  
Rainfall Runoff ◽  
Nonlinear Stochastic Model

Green supplier selection and order allocation: a nonlinear stochastic model

2020 ◽  
Vol 11 (2) ◽  
pp. 111
Author(s):  
Pooria Hashemzahi ◽  
Amirhossein Azadnia ◽  
Masoud Rahiminezhad Galankashi ◽  
Syed Ahmad Helmi ◽  
Farimah Mokhatab Rafiei
Keyword(s):  
Stochastic Model ◽  
Supplier Selection ◽  
Order Allocation ◽  
Green Supplier Selection ◽  
Nonlinear Stochastic Model

scholarly journals Expansion for the Moments of a Nonlinear Stochastic Model

1996 ◽  
Vol 77 (16) ◽  
pp. 3280-3283 ◽  
Author(s):  
A. N. Drozdov ◽  
M. Morillo
Keyword(s):  
Stochastic Model ◽  
Nonlinear Stochastic Model

scholarly journals Linking Nonlinearity and Non-Gaussianity of Planetary Wave Behavior by the Fokker–Planck Equation

2005 ◽  
Vol 62 (7) ◽  
pp. 2098-2117 ◽  
Author(s):  
Judith Berner
Keyword(s):  
Stochastic Model ◽  
Probability Density ◽  
Planetary Wave ◽  
Multiplicative Noise ◽  
Planck Equation ◽  
Fokker Planck Equation ◽  
State Dependent ◽  
Nonlinear Stochastic Model ◽  
Non Gaussian ◽  
Fokker Planck

Abstract To link prominent nonlinearities in the dynamics of 500-hPa geopotential heights to non-Gaussian features in their probability density, a nonlinear stochastic model of atmospheric planetary wave behavior is developed. An analysis of geopotential heights generated by extended integrations of a GCM suggests that a stochastic model and its associated Fokker–Planck equation call for a nonlinear drift and multiplicative noise. All calculations are carried out in the reduced phase space spanned by the leading EOFs. It is demonstrated that this nonlinear stochastic model of planetary wave behavior captures the non-Gaussian features in the probability density function of atmospheric states to a remarkable degree. Moreover, it not only predicts global temporal characteristics, but also the nonlinear, state-dependent divergence of state trajectories. In the context of this empirical modeling, it is discussed on which time scale a stochastic model is expected to approximate the behavior of a continuous deterministic process. The reduced model is then used to determine the importance of the nonlinearities in the drift and the role of the multiplicative noise. While the nonlinearities in the drift are crucial for a good representation of planetary wave behavior, multiplicative (i.e., state dependent) noise is not absolutely essential. It is found that a major contributor to the stochastic component is the Branstator–Kushnir oscillation, which acts as a fluctuating force for physical processes with even longer time scales, like those that project on the Arctic Oscillation pattern. In this model, the oscillation is represented by strongly correlated noise.

Predicting the Cloud Patterns for the Boreal Summer Intraseasonal Oscillation Through a Low-Order Stochastic Model

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Nan Chen ◽  
Andrew J. Majda
Keyword(s):  
Time Series ◽  
Stochastic Model ◽  
Brightness Temperature ◽  
Large Scale ◽  
Hidden Variables ◽  
Boreal Summer ◽  
Ensemble Spread ◽  
Nonlinear Stochastic Model ◽  
Non Gaussian ◽  
Cloud Patterns

AbstractWe assess the predictability limits of the large-scale cloud patterns in the boreal summer intraseasonal variability (BSISO), which are measured by the infrared brightness temperature, a proxy for convective activity. A recent developed nonlinear data analysis technique, nonlinear Laplacian spectrum analysis (NLSA), is applied to the brightness temperature data, defining two spatial modes with high intermittency associated with the BSISO time series. Then a recent developed data-driven physics-constrained low-ordermodeling strategy is applied to these time series. The result is a four dimensional system with two observed BSISO variables and two hidden variables involving correlated multiplicative noise through the nonlinear energyconserving interaction. With the optimal parameters calibrated by information theory, the non-Gaussian fat tailed probability distribution functions (PDFs), the autocorrelations and the power spectrum of the model signals almost perfectly match those of the observed data. An ensemble prediction scheme incorporating an effective on-line data assimilation algorithm for determining the initial ensemble of the hidden variables shows the useful prediction skill in the non-El Niño years is at least 30 days and even reaches 55 days in those years with regular oscillations and the skillful prediction lasts for 18 days in the strong El Niño year (year 1998). Furthermore, the ensemble spread succeeds in indicating the forecast uncertainty. Although the reduced linear model with time-periodic stable-unstable damping is able to capture the non-Gaussian fat tailed PDFs, it is less skillful in forecasting the BSISO in the years with irregular oscillations. The failure of the ensemble spread to include the truth also indicates failure in quantification of the uncertainty. In addition, without the energy-conserving nonlinear interactions, the linear model is sensitive with parameter variations. mcwfnally, the twin experiment with nonlinear stochastic model has comparable skill as the observed data, suggesting the nonlinear stochastic model has significant skill for determining the predictability limits of the large-scale cloud patterns of the BSISO.

scholarly journals Precipitation Nowcasting by a Spectral-Based Nonlinear Stochastic Model

2009 ◽  
Vol 10 (5) ◽  
pp. 1285-1297 ◽  
Author(s):  
Sabino Metta ◽  
Jost von Hardenberg ◽  
Luca Ferraris ◽  
Nicola Rebora ◽  
Antonello Provenzale
Keyword(s):  
Power Spectrum ◽  
Stochastic Model ◽  
Lead Time ◽  
Amplitude Distribution ◽  
Forecast Skill ◽  
Spectral Space ◽  
Type Model ◽  
Stochastic Evolution ◽  
Nonlinear Stochastic Model ◽  
Study Case

Abstract A novel rainfall nowcasting method based on the combination of an empirical nonlinear transformation of measured precipitation fields and the stochastic evolution in spectral space of the transformed fields is introduced. The power spectrum and the amplitude distribution of precipitation are kept constant during the forecast, and a Langevin-type model is used to evolve the Fourier phases. The application of the method to a study case is illustrated, and it is shown that, with this procedure, a forecast skill can be obtained that is superior to those provided by Eulerian or Lagrangian persistence for a lead time of up to two hours.

Nonlinear, stochastic model for energy investment in manufacturing

Energy ◽  
1989 ◽  
Vol 14 (7) ◽  
pp. 421-433 ◽  
Author(s):  
M.S. Pena-Taveras ◽  
A.B. Cambel
Keyword(s):  
Stochastic Model ◽  
Energy Investment ◽  
Nonlinear Stochastic Model
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