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http://dx.doi.org/10.9708/jksci.2020.25.02.031

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

Kim, Kyung Min (Dept. of Computer Engineering, Yeungnam University)
Kwak, Jong Wook (Dept. of Computer Engineering, Yeungnam University)
Publication Information
Journal of the Korea Society of Computer and Information / v.25, no.2, 2020 , pp. 31-39 More about this Journal
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.
Keywords
time series data generation; IoTs; performance evaluation; benchmark; ARIMA;
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