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Conference Paper Locality-based Time Series Data Augmentation for Multi-Sensor Internet Of Things Terminal
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Authors
Kang-Il Choi, JungHee Lee
Issue Date
2022-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.1726-1728
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC55196.2022.9952392
Abstract
Existing approaches for time series data augmentation of deep learning are mostly applied for artificial intelligence applications such as time series classification, time series anomaly detection, and forecasting. In this paper, we propose a locality-based data augmentation for multi-sensor Internet of Things (IoT) terminal. By providing locality-based co-related augmented time series data sets, our approach not only resolves normally collected time series data missing-out problem but also resolves unavoidable error insertion problem of artificial intelligence deep learning compensation.
KSP Keywords
Data Augmentation, Data missing, Data sets, Existing Approaches, Multi-Sensor, Time Series Classification, Time series data, anomaly detection, artificial intelligence applications, deep learning(DL), internet of things(IoT)