Deep learning based anomaly detection in time-series data
Anomaly detection, also called outlier detection, on the multivariate time-series data is applicable to multiple domains. With the proliferation of deep learning-based methods, we aim to leverage on them to tackle anomaly detection, mainly on the field of industry data (server machines and spacecraf...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/137949 |
| _version_ | 1826119544055267328 |
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| author | Zeng, Jinpo |
| author2 | A S Madhukumar |
| author_facet | A S Madhukumar Zeng, Jinpo |
| author_sort | Zeng, Jinpo |
| collection | NTU |
| description | Anomaly detection, also called outlier detection, on the multivariate time-series data is applicable to multiple domains. With the proliferation of deep learning-based methods, we aim to leverage on them to tackle anomaly detection, mainly on the field of industry data (server machines and spacecrafts, which are monitored with multivariate time series). This project proposes an anomaly detection framework, which includes data exploration, data pre-processing, RNN-based models, dynamic threshold selection. The effectiveness of various machine learning technologies such as long short-term memory networks (LSTMs), gated recurrent unit networks (GRUs) and autoencoders (AEs) are examined. Subsequently, for dynamic threshold selection, a non-parametric and computationally efficient approach is proposed. The error threshold pruning is introduced to mitigate false positives. The final result demonstrates the capability of the proposed framework for anomaly detection on the multivariate time-series data in the industry data. |
| first_indexed | 2024-10-01T05:01:46Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/137949 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:01:46Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1379492020-04-20T06:19:01Z Deep learning based anomaly detection in time-series data Zeng, Jinpo A S Madhukumar School of Computer Science and Engineering ASMadhukumar@ntu.edu.sg Engineering::Computer science and engineering Anomaly detection, also called outlier detection, on the multivariate time-series data is applicable to multiple domains. With the proliferation of deep learning-based methods, we aim to leverage on them to tackle anomaly detection, mainly on the field of industry data (server machines and spacecrafts, which are monitored with multivariate time series). This project proposes an anomaly detection framework, which includes data exploration, data pre-processing, RNN-based models, dynamic threshold selection. The effectiveness of various machine learning technologies such as long short-term memory networks (LSTMs), gated recurrent unit networks (GRUs) and autoencoders (AEs) are examined. Subsequently, for dynamic threshold selection, a non-parametric and computationally efficient approach is proposed. The error threshold pruning is introduced to mitigate false positives. The final result demonstrates the capability of the proposed framework for anomaly detection on the multivariate time-series data in the industry data. Bachelor of Engineering (Computer Science) 2020-04-20T06:19:00Z 2020-04-20T06:19:00Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/137949 en SCSE19-0257 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Zeng, Jinpo Deep learning based anomaly detection in time-series data |
| title | Deep learning based anomaly detection in time-series data |
| title_full | Deep learning based anomaly detection in time-series data |
| title_fullStr | Deep learning based anomaly detection in time-series data |
| title_full_unstemmed | Deep learning based anomaly detection in time-series data |
| title_short | Deep learning based anomaly detection in time-series data |
| title_sort | deep learning based anomaly detection in time series data |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/137949 |
| work_keys_str_mv | AT zengjinpo deeplearningbasedanomalydetectionintimeseriesdata |