@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00235674,
 author = {コアンフィ, ウン and 牛, コウ and ギョーム, アボー and 南川, 敦宣},
 book = {第86回全国大会講演論文集},
 issue = {1},
 month = {Mar},
 note = {Real-time monitoring and time-series collecting system often faces to the issue of disruption due to common issues such as hardware/software failure or network disconnection, leading to the problem of missing data or time-delayed data. Online learning-based time-series prediction model trained by those missing data could be affected negative impact and degrade its performance. Recently, a method (namely, ERL) leveraging time-delayed complete data to enhance the time-series representation learning is introduced and shown the efficacy to address this time-delayed issue. In this paper, we present technical extension of ERL to reduce the resource consumption during training process.},
 pages = {127--128},
 publisher = {情報処理学会},
 title = {Time-delay multivariate time series prediction: a technical extension},
 volume = {2024},
 year = {2024}
}