Publication: Impact of Resampling and Deep Learning to Detect Anomaly in Imbalance Time-Series Data
| dc.contributor.author | Saripuddin M. | en_US |
| dc.contributor.author | Suliman A. | en_US |
| dc.contributor.author | Sameon S.S. | en_US |
| dc.contributor.authorid | 57220806580 | en_US |
| dc.contributor.authorid | 25825739000 | en_US |
| dc.contributor.authorid | 36683226000 | en_US |
| dc.date.accessioned | 2023-05-29T09:41:13Z | |
| dc.date.available | 2023-05-29T09:41:13Z | |
| dc.date.issued | 2022 | |
| dc.description | Deep neural networks; Time series; Anomaly detection; Capture time; Data imbalance; Electricity theft detection; Imbalance time series data; Over sampling; Resampling; Resampling technique; Time-series data; Under-sampling; Anomaly detection | en_US |
| dc.description.abstract | In the domain of anomaly detection, it is common that the data presented has lower amount of anomaly cases that cause data imbalance. Resampling technique has been one of the fastest and reliable way to overcome data imbalance but its effectiveness on time series data is yet to be proven. Deep learning is a good approach to work with time series data since it can capture time shift that exists in the data but what if the data is highly imbalance? Thus, this study aims at investigating whether resampling technique and deep learning can work best on highly imbalance time series data. The experiments will be made by applying three famous resampling techniques: SMOTE, ROS and RUS on an ANN algorithm. The ANN is also modified into a deep learning named as DANN by increasing the number of hidden layers. Different training-testing ratio is used since resampling is challenged with underfit and overfit issues. Five evaluation metrics are used to record the result which are the AUC, Accuracy, Recall, Precision and Fl-Score. Consequently, Random Un dersampling with lowest training sample performs the best with the deep neural network model to detect anomaly in imbalance time series data. � 2022 IEEE. | en_US |
| dc.description.nature | Final | en_US |
| dc.identifier.doi | 10.1109/ICCRD54409.2022.9730424 | |
| dc.identifier.epage | 41 | |
| dc.identifier.scopus | 2-s2.0-85127771308 | |
| dc.identifier.spage | 37 | |
| dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127771308&doi=10.1109%2fICCRD54409.2022.9730424&partnerID=40&md5=dc015024ac1575260dc602f2e33302fa | |
| dc.identifier.uri | https://irepository.uniten.edu.my/handle/123456789/27223 | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.source | Scopus | |
| dc.sourcetitle | 2022 IEEE 14th International Conference on Computer Research and Development, ICCRD 2022 | |
| dc.title | Impact of Resampling and Deep Learning to Detect Anomaly in Imbalance Time-Series Data | en_US |
| dc.type | Conference Paper | en_US |
| dspace.entity.type | Publication |