A CDF-based symbolic time-series data mining approach for electricity consumption analysis

I. Chin Wu*, Yi An Chen, Zan Xian Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Electricity is critical for industrial and economic advancement, as well as a driving force for sustainable development. This study collects the energy consumption data of annealing processes from an annealing furnace of a co-operating steel forging plant. We propose a CDF-based symbolic time-series data mining and analytic framework for electricity consumption analysis and prediction of machine operating states by machine-learning techniques. We computed the breakpoint value relying on a density-based notion – namely, the cumulative distribution function (CDF) – to improve the original breakpoint table in the SAX algorithm for symbolizing the time-series data. The main contribution of this work is that the modified SAX algorithm can achieve better prediction the operating state of the machine in comparison to the original SAX algorithm.

Original languageEnglish
Title of host publicationHCI International 2018 – Posters’ Extended Abstracts - 20th International Conference, HCI International 2018, Proceedings
EditorsConstantine Stephanidis
PublisherSpringer Verlag
Number of pages7
ISBN (Print)9783319922843
Publication statusPublished - 2018
Event20th International Conference on HCI, HCI International 2018 - Las Vegas, United States
Duration: 2018 Jul 152018 Jul 20

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


Conference20th International Conference on HCI, HCI International 2018
Country/TerritoryUnited States
CityLas Vegas


  • Cumulative distribution function
  • Electricity consumption analysis
  • Symbolic aggregate approximation
  • Time-series data mining

ASJC Scopus subject areas

  • General Computer Science
  • General Mathematics


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