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Time series forecasting using wavelet-least squares support vector machines and wavelet regression models for monthly stream flow data

This paper presents a review of runoff forecasting method based on hydrological time series data mining. Researchers are developed models for runoff forecasting using the data mining tools and techniques like regression analysis, clustering, artificial neural network (ANN), and support vector machin...

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Main Authors:	Muhammed Pandhiani, Siraj, Shabri, Ani
格式:	Article
出版:	2013
主题:	Q Science
在线阅读:	http://eprints.utm.my/id/eprint/40886/
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id	my.utm.40886
record_format	eprints
spelling	my.utm.408862017-08-16T08:15:20Z http://eprints.utm.my/id/eprint/40886/ Time series forecasting using wavelet-least squares support vector machines and wavelet regression models for monthly stream flow data Muhammed Pandhiani, Siraj Shabri, Ani Q Science This paper presents a review of runoff forecasting method based on hydrological time series data mining. Researchers are developed models for runoff forecasting using the data mining tools and techniques like regression analysis, clustering, artificial neural network (ANN), and support vector machine (SVM), Genetic Algorithms (GA), fuzzy logic and rough set theories. The scientific community has been trying to find out a better approach to solve the issues of flood problems. Time Series Data mining is paying crucial role for the achieving a real time hydrological forecast. Hydrological Time series is an important class of temporal data objects and it can be find out from water resource management and metrological department. A hydrological time series is a collection of observations of hydro and hydrometeorological parameters chronologically. The wide use of hydrological time series data has initiated a great deal of research and development attempts in the field of data mining. Trend, pattern, simulation, similarity measures indexing, segmentation, visualization and prediction carried out by the researchers with the implicit mining from the historical observed data. The critical reviews of the existing hydrological parameter prediction research are briefly explored to identify the present circumstances in hydrological fields and its concerned issues. 2013 Article PeerReviewed Muhammed Pandhiani, Siraj and Shabri, Ani (2013) Time series forecasting using wavelet-least squares support vector machines and wavelet regression models for monthly stream flow data. Open Journal of Statistics, 3 (n/a). pp. 183-194. ISSN 2161-7198
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	Q Science
spellingShingle	Q Science Muhammed Pandhiani, Siraj Shabri, Ani Time series forecasting using wavelet-least squares support vector machines and wavelet regression models for monthly stream flow data
description	This paper presents a review of runoff forecasting method based on hydrological time series data mining. Researchers are developed models for runoff forecasting using the data mining tools and techniques like regression analysis, clustering, artificial neural network (ANN), and support vector machine (SVM), Genetic Algorithms (GA), fuzzy logic and rough set theories. The scientific community has been trying to find out a better approach to solve the issues of flood problems. Time Series Data mining is paying crucial role for the achieving a real time hydrological forecast. Hydrological Time series is an important class of temporal data objects and it can be find out from water resource management and metrological department. A hydrological time series is a collection of observations of hydro and hydrometeorological parameters chronologically. The wide use of hydrological time series data has initiated a great deal of research and development attempts in the field of data mining. Trend, pattern, simulation, similarity measures indexing, segmentation, visualization and prediction carried out by the researchers with the implicit mining from the historical observed data. The critical reviews of the existing hydrological parameter prediction research are briefly explored to identify the present circumstances in hydrological fields and its concerned issues.
format	Article
author	Muhammed Pandhiani, Siraj Shabri, Ani
author_facet	Muhammed Pandhiani, Siraj Shabri, Ani
author_sort	Muhammed Pandhiani, Siraj
title	Time series forecasting using wavelet-least squares support vector machines and wavelet regression models for monthly stream flow data
title_short	Time series forecasting using wavelet-least squares support vector machines and wavelet regression models for monthly stream flow data
title_full	Time series forecasting using wavelet-least squares support vector machines and wavelet regression models for monthly stream flow data
title_fullStr	Time series forecasting using wavelet-least squares support vector machines and wavelet regression models for monthly stream flow data
title_full_unstemmed	Time series forecasting using wavelet-least squares support vector machines and wavelet regression models for monthly stream flow data
title_sort	time series forecasting using wavelet-least squares support vector machines and wavelet regression models for monthly stream flow data
publishDate	2013
url	http://eprints.utm.my/id/eprint/40886/
_version_	1643650584800133120
score	13.251813

Time series forecasting using wavelet-least squares support vector machines and wavelet regression models for monthly stream flow data

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