Time series support vector regression models with missing data treatments for water level prediction

Rise in water level is an important issue because it can be used as an indicator for flood alert. The water level of a river is dependent upon variables such as the month, volume of rainfall, temperature, relative humidity and surface wind. The main purpose of this research is to find a suitable met...

Description complète

Détails bibliographiques
Auteur principal:	Ibrahim, Noraini
Format:	Thèse
Langue:	anglais
Publié:	2014
Sujets:	QA75 Electronic computers. Computer science
Accès en ligne:	http://eprints.utm.my/48022/25/NorainiIbrahimMFC2014.pdf

Documents similaires

Time series support vector regression models with missing data treatments for water lavel prediction
par: Ibrahim, Noraini
Publié: (2014)

Finding kernel function for stock market prediction with support vector regression
par: Chai, Chon Lung
Publié: (2006)

Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction
par: Zuriani, Mustaffa
Publié: (2014)

An enhanced support vector regression -African Buffalo optimisation algorithm for electricity time series forecasting
par: Maijama'a, Inusa Sani
Publié: (2023)

Treatment of hydrological missing data : filling the missing gaps
par: Tang, Weng Yin
Publié: (1994)

The enhanced group method of data handling models for time series forecasting
par: Samsudin, Ruhaidah
Publié: (2012)

Combine holts winter and support vector machines in forecasting time serie
par: Alfa, Mohammed Salisu
Publié: (2013)

Combine holts winter and support vector machines in forecasting time series
par: Salisu, Alfa Mohammed
Publié: (2013)

Robust Random Regression Imputation method for missing data in the presence of outliers
par: John, Ahamefule Happy
Publié: (2013)

Identification of time series components using break for time series components (bftsc) and group for time series components (gftsc) techniques
par: Oloruntoba, Ajare Emmanuel
Publié: (2022)

New bio-inspired barnacle optimizers based least-square support vector machine for time-series prediction of pandemic outbreaks
par: Marzia, Ahmed
Publié: (2024)

Enhanced k-nearest neighbours classification performance based on segmentation and imputation of missing data
par: Saeed, Soobia
Publié: (2022)

Enzyme sub-functional class prediction using multibiological knowledge feature respresentation and twin support vector machine
par: Guramad Singh, Sharon Kaur
Publié: (2013)

Fake review detection using time series
par: Tavakoli, Mohammadali
Publié: (2014)

Combining group method of data handling models using artificial bee colony algorithm for time series forecasting
par: Yahya, Nurhaziyatul Adawiyah
Publié: (2019)

The Impact of Missing Value Methods and Normalization Techniques on the Performance of Data Mining Models
par: Munirah, Yahya
Publié: (2011)

Email categorization using support vector machine
par: Mohd. Daud, Mariah
Publié: (2004)

Robust diagnostics and parameter estimation methods in linear and nonlinear regression based on nu support vector regression for high dimensional data
par: Rashid, Al-Dulaimi Abdullah Mohammed
Publié: (2022)

Hybridization of nonlinear and linear models for time series forecasting
par: Salleh@Sallehuddin, Roselina
Publié: (2010)

Application of EM algorithm on missing categorical data analysis
par: Hasan, Noraini
Publié: (2009)

Support vector machine and its applications for linear and nonlinear regression in the presence of outliers of high dimensional data
par: Sleabi, Waleed Dhhan
Publié: (2016)

The comparative forecast performance of univariate and multivariate model: an application to time series forecasting
par: Sallehuddin, Roselina, et autres
Publié: (2009)

Time series modeling and designing of artifical neural network (ANN) for revenue forecasting
par: Mohd. Yusof, Norfadzlia
Publié: (2005)

Time series modeling of water level at Sulaiman Station, Klang River, Malaysia
par: Galavi, Hadi
Publié: (2010)

Back propagation neural network and non-linear regression models for dengue outbreak prediction
par: Husin, Nor Azura
Publié: (2008)

Empirical Comparison of Techniques for Handling Missing Values
par: Tikla, Salleh Mansour Mohamed
Publié: (2006)

Parliamentary response preparation support mechanism at the ministry level : Ministry of Energy, Water and Communications (MEWC
par: Mustafa, Normas Norhayati
Publié: (2007)

Investigating the Impact of Different Representations of Data on Neural Network and Regression
par: Fallah, Ehab A. Omer El
Publié: (2008)

Grey relational analysis feature selection for cancer classification using support vector machine
par: Sy. Ahmad Ubaidillah, Sharifah Hafizah
Publié: (2014)

Arabic language script and encoding identification with support vector machines and rough set theory
par: Mohamed Sidya, Mohamed Ould
Publié: (2007)

Smooth support vector regression (SSVR) modelling of self-compacting concrete properties
par: Hadiwidodo, Yoyok Setyo
Publié: (2013)

A public transport travel time prediction using least squares support vector machine
par: Sadat Zadeh, Seyed Mojtaba Tafaghod
Publié: (2013)

An efficient real-time terrain data organization and visualization algorithm based on enhanced triangle-based level of detail technique
par: Zamri, Muhamad Najib
Publié: (2006)

Sequence comparison latent semantic analysis and support vector machine to detect remote protein homology
par: Ismail, Surayati
Publié: (2010)

EEG-based emotion classification using wavelet based features and support vector machine classifier
par: M. Razali, Normasliza
Publié: (2015)

Predictive inference with copulas for bivariate data
par: Noryanti, Muhammad
Publié: (2016)

Identifying cancer gene subtypes from gene expression by co-clustering algorithm and support vector machine
par: Machap, Logenthiran
Publié: (2021)

Outlier detection in wireless sensor network based on time series approach
par: Safaei, Mahmood
Publié: (2019)

Improved scheme of e-mail spam classification using meta-heuristics feature selection and support vector machine
par: Elssied Hamed, Nadir Omer Fadl
Publié: (2015)

Improving Time Series Models Prediction Based On Empirical Mode Decomposition Using Stock Market Data
par: Hossain, Mohammad Raquibul
Publié: (2021)