VAR and GSTAR-BASED feature selection in support vector regression for multivariate spatio-temporal forecasting
Multivariate time series modeling is quite challenging particularly var(--highlight-yellow); color: inherit;">in term of diagnostic checking for assumptions required by the underlying model. For that reason, nonparametric approach is rapidly developed to overcome that problem. But, var(--hig...
Saved in:
Main Authors: | Prastyo, D. D., Nabila, F. S., Suhartono, Suhartono, Lee, M. H., Suhermi, N., Soo, F. F. |
---|---|
Format: | Conference or Workshop Item |
Published: |
2019
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/91617/ http://www.dx.doi.org/10.1007/978-981-13-3441-2_4 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Comparison between VAR, GSTAR, FFNN-VAR and FFNN-GSTAR Models for Forecasting Oil Production
by: Suhartono, Suhartono, et al.
Published: (2018) -
Streamflow forecasting using least-squares support vector machines
by: Shabri, Ani, et al.
Published: (2012) -
Comparison between hybrid quantile regression neural network and autoregressive integrated moving average with exogenous variable for forecasting of currency inflow and outflow in bank Indonesia
by: Prastyo, Dedy Dwi, et al.
Published: (2018) -
Generalized Space-Time Autoregressive (GSTAR) for forecasting air pollutant index in Selangor
by: Nur Maisara Mohamed,, et al.
Published: (2023) -
Hybrid SSA-TSR-ARIMA for water demand forecasting
by: Suhartono, Suhartono, et al.
Published: (2018)