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BST-IGT Model: Synthetic Benchmark Generation Technique Maintaining Trend of Time Series Data

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2020, 25(2), pp.31-39
  • DOI : 10.9708/jksci.2020.25.02.031
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : January 28, 2020
  • Accepted : February 11, 2020
  • Published : February 28, 2020

KYUNG MIN KIM 1 Jong Wook Kwak 1

1영남대학교

Accredited

ABSTRACT

In this paper, we introduce a technique for generating synthetic benchmarks based on time series data. Many of the data measured on IoT devices have a time series characteristic that measures numerical changes over time. However, there is a problem that it is difficult to model the data measured over a long period as generalized time series data. To solve this problem, this paper introduces the BST-IGT model. The BST-IGT model separates the entire data into sections that can be easily time-series modeled, collects the generated data into templates, and produces new synthetic benchmarks that share or modify characteristics based on them. As a result of making a new benchmark using the proposed modeling method, we could create a benchmark with multiple aspects by mixing the composite benchmark with the statistical features of the existing data and other benchmarks.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.