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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
- 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)