State of the Art Machine Learning Techniques for Time Series Forecasting: A Survey

Time Series Forecasting is vital for wide range of domains such as financial market forecasting, earthquake forecasting, weather forecasting, electric power demand forecasting and etc. The past 25 years of time series forecasting research that has been reviewed in (Tinbergen Institute Discussion Pap...

Full description

Bibliographic Details
Main Authors:	Nyein Naing, Wai Yan, Htike@Muhammad Yusof, Zaw Zaw
Format:	Conference or Workshop Item
Language:	English
Published:	2015
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/48055/ http://irep.iium.edu.my/48055/ http://irep.iium.edu.my/48055/1/ID_122.pdf

id	iium-48055
recordtype	eprints
spelling	iium-480552018-06-26T02:59:40Z http://irep.iium.edu.my/48055/ State of the Art Machine Learning Techniques for Time Series Forecasting: A Survey Nyein Naing, Wai Yan Htike@Muhammad Yusof, Zaw Zaw T Technology (General) Time Series Forecasting is vital for wide range of domains such as financial market forecasting, earthquake forecasting, weather forecasting, electric power demand forecasting and etc. The past 25 years of time series forecasting research that has been reviewed in (Tinbergen Institute Discussion Paper: International Journal of Forecasting) for the period of 1985 to 2005. Therefore, the purpose of my paper is continue to review the recent 10 years of different state of the machine learning techniques for time series forecasting . The main contribution of this paper is to supply researchers with a cohesive overview of state of the art machine learning techniques (during the period of 2005 to 2015) and to identify possible opportunities for future research. 2015 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/48055/1/ID_122.pdf Nyein Naing, Wai Yan and Htike@Muhammad Yusof, Zaw Zaw (2015) State of the Art Machine Learning Techniques for Time Series Forecasting: A Survey. In: International Conference on Advances Technology in Telecommunication, Broadcasting, and Satellite, 26-27 September, 2015, Jakarta, Indonesia. (In Press) http://telsatech.org/
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Nyein Naing, Wai Yan Htike@Muhammad Yusof, Zaw Zaw State of the Art Machine Learning Techniques for Time Series Forecasting: A Survey
description	Time Series Forecasting is vital for wide range of domains such as financial market forecasting, earthquake forecasting, weather forecasting, electric power demand forecasting and etc. The past 25 years of time series forecasting research that has been reviewed in (Tinbergen Institute Discussion Paper: International Journal of Forecasting) for the period of 1985 to 2005. Therefore, the purpose of my paper is continue to review the recent 10 years of different state of the machine learning techniques for time series forecasting . The main contribution of this paper is to supply researchers with a cohesive overview of state of the art machine learning techniques (during the period of 2005 to 2015) and to identify possible opportunities for future research.
format	Conference or Workshop Item
author	Nyein Naing, Wai Yan Htike@Muhammad Yusof, Zaw Zaw
author_facet	Nyein Naing, Wai Yan Htike@Muhammad Yusof, Zaw Zaw
author_sort	Nyein Naing, Wai Yan
title	State of the Art Machine Learning Techniques for Time Series Forecasting: A Survey
title_short	State of the Art Machine Learning Techniques for Time Series Forecasting: A Survey
title_full	State of the Art Machine Learning Techniques for Time Series Forecasting: A Survey
title_fullStr	State of the Art Machine Learning Techniques for Time Series Forecasting: A Survey
title_full_unstemmed	State of the Art Machine Learning Techniques for Time Series Forecasting: A Survey
title_sort	state of the art machine learning techniques for time series forecasting: a survey
publishDate	2015
url	http://irep.iium.edu.my/48055/ http://irep.iium.edu.my/48055/ http://irep.iium.edu.my/48055/1/ID_122.pdf
first_indexed	2023-09-18T21:08:16Z
last_indexed	2023-09-18T21:08:16Z
_version_	1777411080227651584

State of the Art Machine Learning Techniques for Time Series Forecasting: A Survey

Similar Items