PRIGM: Partial-Regression-Integrated Generic Model for Synthetic Benchmarks Robust to Sensor Characteristics
Kyung Min Kim, Jiung SONG, Jong Wook Kwak · IEICE Transactions on Information and Systems · 2022
We propose a novel synthetic-benchmarks generation model using partial time-series regression, called Partial-Regression-Integrated Generic Model (PRIGM). PRIGM abstracts the unique characteristics of the input sensor data into generic time-series data confirming the generation similarity and evaluating the correctness of the synthetic benchmarks. The experimental results obtained by the proposed model with its formula verify that PRIGM preserves the time-series characteristics of empirical data in complex time-series data within 10.4% on an average difference in terms of descriptive statistics accuracy.