Tailoring High Order Time Discretizations for Use with Spatial Discretizations of Hyperbolic PDEs

2015 ◽  
Author(s):  
Sigal Gottlieb
Keyword(s):  
High Order ◽  
Hyperbolic Pdes ◽  
High Order Time

Simulating the temporal change of the active response of arteries by finite elements with high-order time-integrators

2019 ◽  
Vol 64 (6) ◽  
pp. 1669-1684 ◽  
Author(s):  
Rose Rogin Gilbert ◽  
Matthias Grafenhorst ◽  
Stefan Hartmann ◽  
Zohar Yosibash
Keyword(s):  
Finite Elements ◽  
Temporal Change ◽  
High Order ◽  
Active Response ◽  
Time Integrators ◽  
High Order Time

scholarly journals A Novel Fuzzy Time Series Forecasting Model Based on Multiple Linear Regression and Time Series Clustering

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Yanpeng Zhang ◽  
Hua Qu ◽  
Weipeng Wang ◽  
Jihong Zhao
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Time Series Forecasting ◽  
High Order ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Time Series Clustering ◽  
Proposed Model ◽  
High Order Time

Time series forecasting models based on a linear relationship model show great performance. However, these models cannot handle the the data that are incomplete, imprecise, and ambiguous as the interval-based fuzzy time series models since the process of fuzzification is abandoned. This article proposes a novel fuzzy time series forecasting model based on multiple linear regression and time series clustering for forecasting market prices. The proposed model employs a preprocessing to transform the set of fuzzy high-order time series into a set of high-order time series, with synthetic minority oversampling technique. After that, a high-order time series clustering algorithm based on the multiple linear regression model is proposed to cluster dataset of fuzzy time series and to build the linear regression model for each cluster. Then, we make forecasting by calculating the weighted sum of linear regression models’ results. Also, a learning algorithm is proposed to train the whole model, which applies artificial neural network to learn the weights of linear models. The interval-based fuzzification ensures the capability to deal with the uncertainties, and linear model and artificial neural network enable the proposed model to learn both of linear and nonlinear characteristics. The experiment results show that the proposed model improves the average forecasting accuracy rate and is more suitable for dealing with these uncertainties.

scholarly journals Simulations of kinetic electrostatic electron nonlinear (KEEN) waves with variable velocity resolution grids and high-order time-splitting

2014 ◽  
Vol 68 (10) ◽  
Author(s):  
Bedros Afeyan ◽  
Fernando Casas ◽  
Nicolas Crouseilles ◽  
Adila Dodhy ◽  
Erwan Faou ◽  
...  
Keyword(s):  
High Order ◽  
Variable Velocity ◽  
Time Splitting ◽  
High Order Time
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