Melola Discovery

← Melola Discovery

Jurnal SINTA 2

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

Journal of ICT Research and Applications (JICTRA) · Vol. 11 No. 2 (2017) · pp. 168–184

DOI
10.5614/itbj.ict.res.appl.2017.11.2.4
ISSN
2338-5499, 2337-5787
Published
2017-08-31
Publisher
DRI - ITB
Language
eng

Abstract

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.

Keywords

Collaboration network of the authors

4 authors · 6 co-authorship links · 2 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Amity University Madhya Pradesh
  • Madhav Institute of Technology & Science
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">A Comprehensive Survey of Data Mining Techniques on Time Series Data for Rainfall Prediction</dc:title>
	<dc:creator>Mishra, Neelam</dc:creator>
	<dc:creator>Soni, Hemant Kumar</dc:creator>
	<dc:creator>Sharma, Sanjiv</dc:creator>
	<dc:creator>Upadhyay, A.K.</dc:creator>
	<dc:subject xml:lang="en-US">data mining</dc:subject>
	<dc:subject xml:lang="en-US">intelligent forecasting model</dc:subject>
	<dc:subject xml:lang="en-US">neural network</dc:subject>
	<dc:subject xml:lang="en-US">rainfall forecasting</dc:subject>
	<dc:subject xml:lang="en-US">rainfall and runoff patterns</dc:subject>
	<dc:subject xml:lang="en-US">statistical techniques</dc:subject>
	<dc:subject xml:lang="en-US">time series data mining</dc:subject>
	<dc:subject xml:lang="en-US">weather prediction.</dc:subject>
	<dc:description xml:lang="en-US">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. </dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2017-08-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/2604</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2017.11.2.4</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 11 No. 2 (2017); 168-184</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2017.11.2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/2604/2887</dc:relation>
</oai_dc:dc>