TY - JOUR
AU - Rahman, Khalilur
AU - Anggorowati, Margaretha Ari
AU - Andiojaya, Agung
PY - 2020/06/20
Y2 - 2021/02/25
TI - Prediction and Simulation Spatio-Temporal Support Vector Regression for Nonlinear Data
JF - International Journal on Information and Communication Technology (IJoICT)
JA - ijoict
VL - 6
IS - 1
SE - Computational Science
DO - 10.21108/IJOICT.2020.61.477
UR - https://socj.telkomuniversity.ac.id/ojs/index.php/ijoict/article/view/477
SP - 31-40
AB - 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.
ER -