Acknowledgement
이 논문은 2021년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구임 (No.2021-0-00034, 파편화된 데이터의 적극 활용을 위한 시계열 기반 통합 플랫폼 기술 개발).
References
- Dong-Gyu Jeong.(2017).A Study on IoT-Related Industry Trend. Korea Institute of Information Technology Magazine, 15(1),31-37. https://www.dbpia.co.kr/Journal/articleDetail?nodeId=NODE07187891
- Ahn, H., Chae, H., Jung, W., & Kim, S. (2017, February). Integration of heterogeneous time series gene expression data by clustering on time dimension. In 2017 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 332-335). IEEE. doi: https://doi.org/10.1109/BIGCOMP.2017.7881688
- Yoonjin Hyun, Namgyu Kim.(2018).Text Mining-based Fake News Detection Using News And Social Media Data.The Jounal of Society for e-Business Studies,23(4),19-39. http://www.jsebs.org/jsebs/index.php/jsebs/article/view/338
- Seoha Song, Junhong Kim, Hyungseok Kim, Jaeseon Park, Pilsung Kang.(2019).Development of Early Warning Model for Financial Firms Using Financial and Text Data : A Case Study on Insolvent Bank Prediction. Journal of the Korean Institute of Industrial Engineers, 45(3),248-259. doi: https://doi.org/10.7232/JKIIE.2019.45.3.248
- Stevens, S. S. (1946). On the theory of scales of measure- ment. Science, 103(2684), 677-680. doi: https://doi.org/10.1126/science.103.2684.677
- Matthew Renze. Nominal, Ordinal, Interval, and Ratio Data. https://matthewrenze.com/articles/the-four-subtypes-of-data-in-data-s cience/ (accessed June 15. 2019).
- Kreindler, D. M., & Lumsden, C. J. (2016). The effects of the irregular sample and missing data in time series analysis. In Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data (pp. 149-172). CRC Press. https://psycnet.apa.org/record/2007-00569-003
- Eden Kim, Seok-gap Seok, Seung-cheol Son, & Byeong-tak Lee. (2021). Technical Trends of Time-Series Data Imputation. Electronics and Telecommunications Trends, 36(4), 145-153. doi: https://doi.org/10.22648/ETRI.2021.J.360414
- Won Seok Lee, Hyun Hee Kang.(2020).Interpretable convolutional neural network model for yield prediction in semiconductor fabrication.Journal of the Korean Data And Information Science Society,31(5),691-720. doi: https://doi.org/10.7465/jkdi.2020.31.5.691
- Kang-hyeon Shin, Kyo-hong Jin.(2021).Irregularly-Sampled time Series Correction Method for Anomaly detection in Manufacturing Facility. Proceedings of the Korean Institute of Information and Commucation Sciences Conference,25(2),85-88. https://koreascience.kr/article/CFKO202132348514233.page
- Yue, Z., Wang, Y., Duan, J., Yang, T., Huang, C., Tong, Y., & Xu, B. (2022, June). Ts2vec: Towards universal representation of time series. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 8, pp. 8980-8987). doi: https://doi.org/10.48550/arXiv.2106.10466
- Jin, H. Y., Jung, E. S., & Lee, D. (2020). High-performance IoT streaming data prediction system using Spark: a case study of air pollution. Neural Computing and Applications, 32(17), 13147-13154. doi: https://doi.org/10.1007/s00521-019-04678-9
- Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. doi: https://doi.org/10.1162/neco.1997.9.8.1735
- Xue, J., Huang, Q., Wu, S., & Nagao, T. (2022). LSTM-Autoencoder Network for the Detection of Seismic Electric Signals. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-12. doi: 10.1109/TGRS.2022.3183389
- Detecting Mobile Traffic Anomalies Through Physical Control Channel Fingerprinting: A Deep Semi-Supervised Approach - Scientific Figure on ResearchGate. Available from: https://www.researchgate. net/figure/LSTM-Autoencoder-for-Anomaly-Detection_fig2_336594630 [accessed 13 Nov, 2022] doi: 10.1109/ACCESS.2019.2947742
- Du, Q., Gu, W., Zhang, L., & Huang, S. L. (2018, November). Attention-based LSTM-CNNs for time-series classification. In Proceedings of the 16th ACM conference on embedded networked sensor systems (pp. 410-411). doi: https://doi.org/10.1145/3274783.3275208
- Zhao, B., Lu, H., Chen, S., Liu, J., & Wu, D. (2017). Convolutional neural networks for time series classification. Journal of Systems Engineering and Electronics, 28(1), 162-169. doi: https://doi.org/10.21629/JSEE.2017.01.18
- Wibawa, A. P., Utama, A. B. P., Elmunsyah, H., Pujianto, U., Dwiyanto, F. A., & Hernandez, L. (2022). Time-series analysis with smoothed Convolutional Neural Network. Journal of big Data, 9(1), 1-18. doi: https://doi.org/10.1186/s40537-022-00599-y
- Youngjun Jang, Jiho Kim, Hongchul Lee. (2022). A Proposal of Sensor-based Time Series Classification Model using Explainable Convolutional Neural Network. Journal of the Korea Society of Computer and Information , 27(5), 55-67. doi: 10.9708/jksci.2022.27.05.055