A Comprehensive Survey of Data Mining Techniques on Time Series Data for Rainfall Prediction

Autor:	Neelam Mishra, Hemant Kumar Soni, Sanjiv Sharma, A.K. Upadhyay
Jazyk:	angličtina
Rok vydání:	2017
Předmět:	data mining intelligent forecasting model neural network rainfall forecasting rainfall and runoff patterns statistical techniques Telecommunication TK5101-6720 Information technology T58.5-58.64
Zdroj:	Journal of ICT Research and Applications, Vol 11, Iss 2 (2017)
Druh dokumentu:	article
ISSN:	2337-5787 2338-5499
DOI:	10.5614/itbj.ict.res.appl.2017.11.2.4
Popis:	Time series data available in huge amounts can be used in decision-making. Such time series data can be converted into information to be used for forecasting. Various techniques are available for prediction and forecasting on the basis of time series data. Presently, the use of data mining techniques for this purpose is increasing day by day. In the present study, a comprehensive survey of data mining approaches and statistical techniques for rainfall prediction on time series data was conducted. A detailed comparison of different relevant techniques was also conducted and some plausible solutions are suggested for efficient time series data mining techniques for future algorithms.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/3663133254ed4292b0c210154faa7877 Zobrazit plný text záznamu View record in DOAJ