共 12 条
- [1] A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data [J]. APPLIED SCIENCES-BASEL, 2021, 11 (12):
- [2] Phase I Analysis of Nonlinear Profiles Using Anomaly Detection Techniques [J]. APPLIED SCIENCES-BASEL, 2023, 13 (04):
- [3] Outlier Detection with Explanations on Music Streaming Data: A Case Study with Danmark Music Group Ltd. [J]. APPLIED SCIENCES-BASEL, 2021, 11 (05): : 1 - 14
- [4] Hybrid Machine Learning-Statistical Method for Anomaly Detection in Flight Data [J]. APPLIED SCIENCES-BASEL, 2022, 12 (20):
- [5] Semi-Supervised Time Series Anomaly Detection Based on Statistics and Deep Learning [J]. APPLIED SCIENCES-BASEL, 2021, 11 (15):
- [6] Online Forecasting and Anomaly Detection Based on the ARIMA Model [J]. APPLIED SCIENCES-BASEL, 2021, 11 (07):
- [7] Anomaly Detection Method for Multivariate Time Series Data of Oil and Gas Stations Based on Digital Twin and MTAD-GAN [J]. APPLIED SCIENCES-BASEL, 2023, 13 (03):
- [8] MST-VAE: Multi-Scale Temporal Variational Autoencoder for Anomaly Detection in Multivariate Time Series [J]. APPLIED SCIENCES-BASEL, 2022, 12 (19):
- [9] Low-Cost Active Anomaly Detection with Switching Latency [J]. APPLIED SCIENCES-BASEL, 2021, 11 (07):
- [10] Is It Worth It? Comparing Six Deep and Classical Methods for Unsupervised Anomaly Detection in Time Series [J]. APPLIED SCIENCES-BASEL, 2023, 13 (03):