scholarly journals On an Efficient Multiple Time-Step Monte Carlo Simulation of the SABR Model

2016 ◽  
Author(s):  
Alvaro Leitao Rodriguez ◽  
Lech A. Grzelak ◽  
Cornelis W. Oosterlee
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Multiple Time ◽  
Time Step ◽  
Multiple Time Step ◽  
Sabr Model

scholarly journals On an efficient multiple time step Monte Carlo simulation of the SABR model

2017 ◽  
Vol 17 (10) ◽  
pp. 1549-1565 ◽  
Author(s):  
Álvaro Leitao ◽  
Lech A. Grzelak ◽  
Cornelis W. Oosterlee
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Multiple Time ◽  
Time Step ◽  
Multiple Time Step ◽  
Sabr Model

Multiple “time step” Monte Carlo

2002 ◽  
Vol 117 (18) ◽  
pp. 8203-8207 ◽  
Author(s):  
Balázs Hetényi ◽  
Katarzyna Bernacki ◽  
B. J. Berne
Keyword(s):  
Monte Carlo ◽  
Multiple Time ◽  
Time Step ◽  
Multiple Time Step

scholarly journals Multiple Time-Step Dual-Hamiltonian Hybrid Molecular Dynamics – Monte Carlo Canonical Propagation Algorithm

2016 ◽  
Vol 12 (4) ◽  
pp. 1449-1458 ◽  
Author(s):  
Yunjie Chen ◽  
Seyit Kale ◽  
Jonathan Weare ◽  
Aaron R. Dinner ◽  
Benoît Roux
Keyword(s):  
Molecular Dynamics ◽  
Monte Carlo ◽  
Multiple Time ◽  
Time Step ◽  
Propagation Algorithm ◽  
Multiple Time Step

scholarly journals Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3517 ◽  
Author(s):  
Anh Ngoc-Lan Huynh ◽  
Ravinesh C. Deo ◽  
Duc-Anh An-Vo ◽  
Mumtaz Ali ◽  
Nawin Raj ◽  
...  
Keyword(s):  
Deep Learning ◽  
Solar Radiation ◽  
Short Term Memory ◽  
Global Solar Radiation ◽  
Multiple Time ◽  
Short Term ◽  
Time Step ◽  
Term Memory ◽  
Multiple Time Step ◽  
Long Short Term Memory

This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy).

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