eprintid: 6204
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/62/04
datestamp: 2023-11-09 16:17:57
lastmod: 2023-11-09 16:17:57
status_changed: 2023-11-09 16:05:14
type: article
metadata_visibility: show
creators_name: Taib, S.M.
creators_name: Bakar, A.A.
creators_name: Hamdan, A.R.
creators_name: Abdullah, S.M.S.
title: Classifying weather time series using featurebased approach
ispublished: pub
note: cited By 5
abstract: Classification of weather time series is beneficial for weather forecasting problem. The classification can assist in identifying weather patterns for certain periods. In addition, extracting patterns from weather time series provides useful insights about the weather conditions to the domain experts. In this paper, we present the classification of weather time series using feature based approach that extracts feature vectors from the time series and performs the classification based on local and global features. The experimental results show that feature-based method with random forest performs well with more number of subsequences and may achieve comparable results with other methods.
date: 2015
publisher: International Center for Scientific Research and Studies
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84949814918&partnerID=40&md5=c22cc76cdda2d8a75fb5acfdeea5b29d
full_text_status: none
publication: International Journal of Advances in Soft Computing and its Applications
volume: 7
number: Specia
pagerange: 56-71
refereed: TRUE
issn: 20748523
citation:   Taib, S.M. and Bakar, A.A. and Hamdan, A.R. and Abdullah, S.M.S.  (2015) Classifying weather time series using featurebased approach.  International Journal of Advances in Soft Computing and its Applications, 7 (Specia).  pp. 56-71.  ISSN 20748523