@inproceedings{72dafd1be3474de285dff427d31c9b20,
title = "Univariate Time Series Data Correction Method using LSTM Autoencoder with Temporal Distance",
abstract = "High-quality data is essential to increase the reliability of machine learning-based prediction models. For time series data, anomalies significantly reduce the accuracy of prediction models. In this paper, we propose a novel time series data correction method that converts abnormal values of univariate time series data into normal ones. For anomaly detection and correction, we utilize the LSTM Autoencoder model, where we propose a new weight function that considers temporal distance. Through experiments using the open NAB data, we show that our proposed method is superior to the recent conventional methods.",
keywords = "anomaly detection, data correction, data quality, deep learning, LSTM Autoencoder, time series data",
author = "Sohyeon Yun and Kim, {Han Joon}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024 ; Conference date: 19-02-2024 Through 22-02-2024",
year = "2024",
doi = "10.1109/ICAIIC60209.2024.10463512",
language = "English",
series = "6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "648--652",
booktitle = "6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024",
address = "United States",
}