Short term forecasting based on hybrid least squares support vector machines

Short term forecasting based on hybrid least squares support vector machines

Flood is one of the common natural disasters that have caused universal damage throughout the world. Due to that matter, reliable flood forecasting is crucial for the purpose of preventing loss of life and minimizing property damage. In this study, hybrid Least Squares Support Vector Machines (LSSVM...

Full description

Bibliographic Details
Main Authors: Zuriani, Mustaffa, M. H., Sulaiman, Ernawan, Ferda, Noorhuzaimi, Mohd Noor
Format: Article
Language:English
Published: American Scientific Publisher 2018
Subjects:
QA76 Computer software
Online Access:http://umpir.ump.edu.my/id/eprint/19448/
http://umpir.ump.edu.my/id/eprint/19448/
http://umpir.ump.edu.my/id/eprint/19448/
http://umpir.ump.edu.my/id/eprint/19448/1/29.%20Short%20Term%20Forecasting%20based%20on%20Hybrid%20Least%20Squares%20Support%20Vector%20Machines1.pdf
id ump-19448
recordtype eprints
spelling ump-194482018-11-13T01:45:40Z http://umpir.ump.edu.my/id/eprint/19448/ Short term forecasting based on hybrid least squares support vector machines Zuriani, Mustaffa M. H., Sulaiman Ernawan, Ferda Noorhuzaimi, Mohd Noor QA76 Computer software Flood is one of the common natural disasters that have caused universal damage throughout the world. Due to that matter, reliable flood forecasting is crucial for the purpose of preventing loss of life and minimizing property damage. In this study, hybrid Least Squares Support Vector Machines (LSSVM) with four meta-heuristic algorithms viz. Grey Wolf Optimizer (GWO-LSSVM), Cuckoo Search (CS-LSSVM), Genetic Algorithm (GA-LSSVM) and Differential Evolution (DE-LSSVM) are presented for a week ahead water level forecasting. The employed meta-heuristic algorithms are individually served as an optimization tool for LSSVM and later, the forecasting is proceeded by LSSVM. This study assesses the performance of each hybrid algorithms based on three statistical indices viz. Mean Square Error (MSE), Root Mean Square Percentage Error (RMSPE) and Theil’s U which is realized on raw and normalized data set. Later, the performance of each identified hybrid algorithm is analyzed and discussed. From the simulations, it is demonstrated that all the identified algorithms are able to produce better forecasting result by using normalized time series data. American Scientific Publisher 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/19448/1/29.%20Short%20Term%20Forecasting%20based%20on%20Hybrid%20Least%20Squares%20Support%20Vector%20Machines1.pdf Zuriani, Mustaffa and M. H., Sulaiman and Ernawan, Ferda and Noorhuzaimi, Mohd Noor (2018) Short term forecasting based on hybrid least squares support vector machines. Advanced Science Letters, 24 (10). pp. 7455-7460. ISSN 1936-6612 https://doi.org/10.1166/asl.2018.12958 DOI: 10.1166/asl.2018.12958
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Zuriani, Mustaffa
M. H., Sulaiman
Ernawan, Ferda
Noorhuzaimi, Mohd Noor
Short term forecasting based on hybrid least squares support vector machines
description Flood is one of the common natural disasters that have caused universal damage throughout the world. Due to that matter, reliable flood forecasting is crucial for the purpose of preventing loss of life and minimizing property damage. In this study, hybrid Least Squares Support Vector Machines (LSSVM) with four meta-heuristic algorithms viz. Grey Wolf Optimizer (GWO-LSSVM), Cuckoo Search (CS-LSSVM), Genetic Algorithm (GA-LSSVM) and Differential Evolution (DE-LSSVM) are presented for a week ahead water level forecasting. The employed meta-heuristic algorithms are individually served as an optimization tool for LSSVM and later, the forecasting is proceeded by LSSVM. This study assesses the performance of each hybrid algorithms based on three statistical indices viz. Mean Square Error (MSE), Root Mean Square Percentage Error (RMSPE) and Theil’s U which is realized on raw and normalized data set. Later, the performance of each identified hybrid algorithm is analyzed and discussed. From the simulations, it is demonstrated that all the identified algorithms are able to produce better forecasting result by using normalized time series data.
format Article
author Zuriani, Mustaffa
M. H., Sulaiman
Ernawan, Ferda
Noorhuzaimi, Mohd Noor
author_facet Zuriani, Mustaffa
M. H., Sulaiman
Ernawan, Ferda
Noorhuzaimi, Mohd Noor
author_sort Zuriani, Mustaffa
title Short term forecasting based on hybrid least squares support vector machines
title_short Short term forecasting based on hybrid least squares support vector machines
title_full Short term forecasting based on hybrid least squares support vector machines
title_fullStr Short term forecasting based on hybrid least squares support vector machines
title_full_unstemmed Short term forecasting based on hybrid least squares support vector machines
title_sort short term forecasting based on hybrid least squares support vector machines
publisher American Scientific Publisher
publishDate 2018
url http://umpir.ump.edu.my/id/eprint/19448/
http://umpir.ump.edu.my/id/eprint/19448/
http://umpir.ump.edu.my/id/eprint/19448/
http://umpir.ump.edu.my/id/eprint/19448/1/29.%20Short%20Term%20Forecasting%20based%20on%20Hybrid%20Least%20Squares%20Support%20Vector%20Machines1.pdf
first_indexed 2023-09-18T22:27:45Z
last_indexed 2023-09-18T22:27:45Z
_version_ 1777416080190341120

Similar Items