Pattern recognition using enhanced non-linear time-series models for predicting dynamic real-time decision making environments Online publication date: Mon, 30-Jul-2012
by S. Uma; A. Chitra
International Journal of Business Information Systems (IJBIS), Vol. 11, No. 1, 2012
Abstract: The abundance of data and importance of knowledge extraction to foresee the future has made time dependent data analysis an inevitable and challenging task in all areas of science and engineering. High dimensionality and the presence of noise in the non-linear time-series data makes it difficult for the existing clustering algorithms to produce efficient results. Hence, two approaches for time series representation (TSR) techniques by name hybrid dimensionality reduction (HDR) and extended hybrid dimensionality reduction (EHDR) and high low non-overlapping (HLN) clustering algorithm that produces efficient results by controlling noise and reducing the dimensionality optimally are proposed. A comparison of the experimental results on intraday non-linear stock data sets to predict the similarity in their intraday behaviour using K-means clustering algorithm with MINDIST as distance measure using symbolic aggregate approximation (SAX) and HLN using HDR and EHDR has proved that EHDR and HDR TSRs outperforms the other models.
Online publication date: Mon, 30-Jul-2012
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Information Systems (IJBIS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org