Improvement algorithms of perceptually important point identification for time series data mining

Tak Chung Fu, Ying Kit Hung, Fu Lai Korris Chung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)

Abstract

In the field of time series data mining, the concept of the Perceptually Important Point (PIP) identification process is proposed for financial time series pattern matching and it is then found suitable for time series dimensionality reduction and representation. Its strength is on preserving the overall shape of the time series by identifying the salient points in it. With the rise of Big Data, time series data contributes a major proportion, especially on the data which generates by sensors in the Internet of Things (IoT) environment. According to the nature of PIP identification and the successful applications in the past years, it is worth to further explore the opportunity to apply PIP in time series 'Big Data'. However, the performance of PIP identification is always considered as the limitation when dealing with 'Big' time series data. In this paper, two improvement algorithms namely Caching and Splitting algorithms are proposed. Significant improvement in term of speed is obtained by these improvement algorithms.

Original languageEnglish
Title of host publicationIEEE 4th International Conference on Soft Computing and Machine Intelligence, ISCMI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages11-15
Number of pages5
ISBN (Electronic)9781538613146
DOIs
Publication statusPublished - 1 Feb 2018
Event4th IEEE International Conference on Soft Computing and Machine Intelligence, ISCMI 2017 - Mauritius, Mauritius
Duration: 23 Nov 201724 Nov 2017

Publication series

NameIEEE 4th International Conference on Soft Computing and Machine Intelligence, ISCMI 2017
Volume2018-January

Conference

Conference4th IEEE International Conference on Soft Computing and Machine Intelligence, ISCMI 2017
CountryMauritius
CityMauritius
Period23/11/1724/11/17

Keywords

  • Perceptually Important Point identification
  • performance analysis
  • PIP
  • time series data mining

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computational Mathematics
  • Modelling and Simulation
  • Artificial Intelligence

Cite this