Digital Library[ Search Result ]
A GRU-based Time-Series Forecasting Method using Patching
http://doi.org/10.5626/JOK.2024.51.7.663
Time series forecasting plays a crucial role in decision-making within various fields. Two recent approaches, namely, the patch time series Transformer (PatchTST) and the long-term time series foraging linear (LTSF-Linear) of the MLP structure have shown promising performance in this area. However, PatchTST requires significant time for both model training and inference, while LTSF-Linear has limited capacity due to its simplistic structure. To address these limitations, we propose a new approach called patch time series GRU (PatchTSG). By leveraging a Gated Recurrent Unit (GRU) on the patched data, PatchTSG reduces the training time and captures valuable information from the time series data. Compared to PatchTST, PatchTSG achieves an impressive reduction in learning time (up to 82%) and inference time (up to 46%).
A GCN-based Time-Series Data Anomaly Detection Method using Sensor-specific Time Lagged Cross Correlation
Kangwoo Lee, Yunyeong Kim, Sungwon Jung
http://doi.org/10.5626/JOK.2023.50.9.805
Anomaly detection of equipment through time series data is a very important because it can prevent further damage and contribute to productivity improvement. Although research studies on time series data anomaly detection are being actively conducted, but they have the following restrictions. First, unnecessary false alarms occur because correlations with other sensors are not considered. Second, although complete graph modeling and GAT have been applied to analyze the correlation of each sensor, this method requires a lot of time due to the increase in unnecessary operations. In this paper, we propose SC-GCNAD(Sensor-specific Correlation GCN Anomaly Detection) to address these problems. SC-GCNAD can analyze the exact correlation of each sensor by applying TLCC that reflects characteristics of time series data. It utilize GCN with excellent model expressiveness. As a result, SC-GCNAD can improve F1-Score by up to 6.37% and reduce analysis time by up to 95.31% compared to the baseline model.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr