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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王勝德(Sheng-De Wang) | |
| dc.contributor.author | Ting-Wei Zhang | en |
| dc.contributor.author | 張廷維 | zh_TW |
| dc.date.accessioned | 2021-06-15T12:42:21Z | - |
| dc.date.available | 2020-08-25 | |
| dc.date.copyright | 2020-08-25 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-11 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/50476 | - |
| dc.description.abstract | 物聯網中的傳感器設備常以時間序列的形式提供數據，例如橋樑振動，溫度，人體生理數據和空氣品質。本文提出了一種可以同時考慮時間序列的重建特徵和時間依賴性之異常檢測模型。所提出的模型基於帶有預測網絡的自動編碼器，該網絡可以即時計算每個時間戳上的異常分數。此外，考慮到每個時間序列之間的異常分數可能會根據環境因素而變化，我們設計了一個動態閾值演算法來為每個單變量時間序列提供一個個別的動態閾值。我們所提出帶有動態閾值演算法的深度學習模型在YahooWebscope數據集中的A1真實基準和Numenta異常基準（NAB）數據集上取得了良好的結果。 | zh_TW |
| dc.description.abstract | Sensor devices in Internet of Things often provide data in the form of time series, such as bridge vibrations, temperatures, human physiological data and air quality. The thesis proposes an anomaly detection model that can simultaneously considers the reconstruction feature and temporal dependence oftime series. The proposed model is based on an auto encoder with a prediction network, which can instantly calculate the anomaly score at each time stamp. In addition, to consider that the anomaly scores among each time series may vary according to environmental factors, we designed a dynamic threshold algorithm to provide an individual dynamic threshold for each univariate time series. The proposed deep learning model with the dynamic threshold algorithm has been shown to achieve good results on the A1 real benchmark in the Yahoo Webscope dataset and Numenta Anomaly Benchmark (NAB) dataset. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T12:42:21Z (GMT). No. of bitstreams: 1 U0001-1108202013085300.pdf: 2951782 bytes, checksum: 4894abc976dfaee438c5ed46b020cb34 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | Contents 口試委員會審定書iii 誌謝v 摘要vii Abstract ix 1 Introduction 1 2 Related Work 5 2.1 Traditional Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Deep Learning Method . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3 Approach 9 3.1 Time Series Autoencoder . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.1.1 Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.1.2 Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.2 Dynamic Thresholding Detector . . . . . . . . . . . . . . . . . . . . . . 13 4 Experiments 17 4.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.1.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.1.2 Hyperparameter . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.1.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.2 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.2.1 Experiment with Yahoo Data Set . . . . . . . . . . . . . . . . . . 20 4.2.2 Experiment with NAB Data Set . . . . . . . . . . . . . . . . . . 21 4.2.3 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 5 Conclusion 25 Bibliography 27 | |
| dc.language.iso | en | |
| dc.title | 自編碼器與動態閾值用於單變量時間序列之異常偵測 | zh_TW |
| dc.title | Anomaly Detection in Univariate Time Series with An Autoencoder and Dynamic Thresholding | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 雷欽隆(Chin-Laung Lei),于天立(Tian-Li Yu),呂欣澤(Hsin-Tse Lu) | |
| dc.subject.keyword | 異常偵測,時間序列,動態閾值,自編碼器,非監督, | zh_TW |
| dc.subject.keyword | Anomaly Detection,Time Series,Dynamic Threshold,Autoencoder,Unsupervised, | en |
| dc.relation.page | 30 | |
| dc.identifier.doi | 10.6342/NTU202002924 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-08-11 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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