scholarly journals Delimiting the spatio-temporal uncertainty of climate-sensitive forest productivity projections using Support Vector Regression

2021 ◽  
Vol 128 ◽  
pp. 107820
Author(s):  
M.A. González-Rodríguez ◽  
U. Diéguez-Aranda
Keyword(s):  
Support Vector Regression ◽  
Forest Productivity ◽  
Support Vector ◽  
Temporal Uncertainty ◽  
Spatio Temporal

scholarly journals Prediction and Simulation Spatio-Temporal Support Vector Regression for Nonlinear Data

2020 ◽  
Vol 6 (1) ◽  
pp. 31
Author(s):  
Khalilur Rahman ◽  
Margaretha Ari Anggorowati ◽  
Agung Andiojaya
Keyword(s):  
Support Vector Regression ◽  
Simulation Models ◽  
Percentage Error ◽  
Average Error ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Average Percentage ◽  
Spatio Temporal ◽  
Average Percentage Error ◽  
Prediction And Simulation

<p>Spatio-temporal model forecasting method is a forecasting model that combines forecasting with a function of time and space.  This method is expected to be able to answer the challenge to produce more accurate and representative forecasting. Using the ability of method Support Vector Regression in dealing with data that is mostly patterned non-linear premises n adding a spatial element in the model of forecasting in the form of a model forecasting Spatio- Temporal. Some simulations have done with generating data that follows the Threshold Autoregressive model. The models are correlated into spatial points generated by several sampling methods. Simulation models are generated to comparing the accuracy between model Spatio-Temporal Support Vector Regression and model  ARIMA  based on  Mean  Error,  Mean Average Error, Root Mean Square Error, and Mean Average Percentage Error. Based on the evaluation results, it is shown that forecasting with the Spatio-Temporal Support Vector Regression model has better accuracy than forecasting ARIMA.</p>

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection

Data Analytics to Help Improve Hydraulic Fracture Geometry Prediction Using Support Vector Regression: Case Study

2020 ◽  
Author(s):  
Avinash Wesley ◽  
Bharat Mantha ◽  
Ajay Rajeev ◽  
Aimee Taylor ◽  
Mohit Dholi ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Hydraulic Fracture ◽  
Data Analytics ◽  
Support Vector ◽  
Fracture Geometry
Sign in / Sign up

Export Citation Format

Share Document