@inproceedings{dce608f989634c718edc9a353da6487b,
title = "Multivariate Time-series Data Correction by combining Attention-based LSTM and GAN Model",
abstract = "High-quality data can increase the reliability of machine learning-based prediction models. In our work, we propose a novel method for data correction to improve the quality of multivariate time-series data. For this, we use a LSTM-based VAE-GAN for anomaly detection and an Attention-based LSTM model for data correction. Through experiments using Secure Water Treatment (SWaT) data, we show that the proposed correction method is superior to previous correction methods.",
keywords = "Data Correction, Data Quality, GAN, Multivariate Time-series",
author = "Hanseok Jeong and Jueun Jeong and Jonghoon Chun and Kim, {Han Joon}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 9th International Conference on Applied System Innovation, ICASI 2023 ; Conference date: 21-04-2023 Through 25-04-2023",
year = "2023",
doi = "10.1109/ICASI57738.2023.10179548",
language = "English",
series = "2023 9th International Conference on Applied System Innovation, ICASI 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "211--213",
editor = "Shoou-Jinn Chang and Sheng-Joue Young and Lam, {Artde Donald Kin-Tak} and Liang-Wen Ji and Prior, {Stephen D.}",
booktitle = "2023 9th International Conference on Applied System Innovation, ICASI 2023",
address = "United States",
}