IEICE TRANSACTIONS on Information
PRIGM: Partial-Regression-Integrated Generic Model for Synthetic Benchmarks Robust to Sensor Characteristics
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E105-D No.7 pp.1330-1334
- Publication Date
- 2022/07/01
- Publicized
- 2022/04/04
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2021EDL8113
- Type of Manuscript
- LETTER
- Category
- Data Engineering, Web Information Systems
Authors
Kyungmin KIM
Yeungnam University
Jiung SONG
SYMYOO CO., LTD.
Jong Wook KWAK
Yeungnam University
Keyword
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Kyungmin KIM, Jiung SONG, Jong Wook KWAK, "PRIGM: Partial-Regression-Integrated Generic Model for Synthetic Benchmarks Robust to Sensor Characteristics" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 7, pp. 1330-1334, July 2022, doi: 10.1587/transinf.2021EDL8113.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDL8113/_p
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@ARTICLE{e105-d_7_1330,
author={Kyungmin KIM, Jiung SONG, Jong Wook KWAK, },
journal={IEICE TRANSACTIONS on Information},
title={PRIGM: Partial-Regression-Integrated Generic Model for Synthetic Benchmarks Robust to Sensor Characteristics},
year={2022},
volume={E105-D},
number={7},
pages={1330-1334},
abstract={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.},
keywords={},
doi={10.1587/transinf.2021EDL8113},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - PRIGM: Partial-Regression-Integrated Generic Model for Synthetic Benchmarks Robust to Sensor Characteristics
T2 - IEICE TRANSACTIONS on Information
SP - 1330
EP - 1334
AU - Kyungmin KIM
AU - Jiung SONG
AU - Jong Wook KWAK
PY - 2022
DO - 10.1587/transinf.2021EDL8113
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2022
AB - 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.
ER -
English
