東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Methods for Event Time Series Predic...

Liu, Siqi.

FindBook

Google Book

Amazon

博客來

Methods for Event Time Series Prediction and Anomaly Detection.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Methods for Event Time Series Prediction and Anomaly Detection./
作者:	Liu, Siqi.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	144 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-09, Section: B.
Contained By:	Dissertations Abstracts International82-09B.
標題:	International conferences. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28370948
ISBN:	9798582554981

Methods for Event Time Series Prediction and Anomaly Detection.
Liu, Siqi.

Methods for Event Time Series Prediction and Anomaly Detection. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 144 p.

Source: Dissertations Abstracts International, Volume: 82-09, Section: B.

Thesis (Ph.D.)--University of Pittsburgh, 2020.

This item must not be sold to any third party vendors.

Event time series are sequences of events occurring in continuous time. They arise in many real-world problems and may represent, for example, posts in social media, administrations of medications to patients, or adverse events, such as episodes of atrial fibrillation or earthquakes. In this work, we study and develop methods for prediction and anomaly detection on event time series. We study two general approaches. The first approach converts event time series to regular time series of counts via time discretization. We develop methods relying on (a) nonparametric time series decomposition and (b) dynamic linear models for regular time series. The second approach models the events in continuous time directly. We develop methods relying on point processes. For prediction, we develop a new model based on point processes to combine the advantages of existing models. It is flexible enough to capture complex dependency structures between events, while not sacrificing applicability in common scenarios. For anomaly detection, we develop methods that can detect new types of anomalies in continuous time and that show advantages compared to time discretization.

ISBN: 9798582554981Subjects--Topical Terms:

3558960
International conferences.
Subjects--Index Terms:

Informatics

Methods for Event Time Series Prediction and Anomaly Detection.
LDR:02235nmm a2200313 4500 001 2282930
005 20211022115802.5
008 220723s2020 ||||||||||||||||| ||eng d
020 $a 9798582554981
035 $a (MiAaPQ)AAI28370948
035 $a (MiAaPQ)Pittsburgh39469
035 $a AAI28370948
040 $a MiAaPQ $c MiAaPQ
100 1 $a Liu, Siqi. $3 3561809
245 1 0 $a Methods for Event Time Series Prediction and Anomaly Detection.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 144 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-09, Section: B.
500 $a Advisor: Poczos, Barnabas;Litman, Diane;Kovashka, Adriana;Hauskrecht, Milos.
502 $a Thesis (Ph.D.)--University of Pittsburgh, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Event time series are sequences of events occurring in continuous time. They arise in many real-world problems and may represent, for example, posts in social media, administrations of medications to patients, or adverse events, such as episodes of atrial fibrillation or earthquakes. In this work, we study and develop methods for prediction and anomaly detection on event time series. We study two general approaches. The first approach converts event time series to regular time series of counts via time discretization. We develop methods relying on (a) nonparametric time series decomposition and (b) dynamic linear models for regular time series. The second approach models the events in continuous time directly. We develop methods relying on point processes. For prediction, we develop a new model based on point processes to combine the advantages of existing models. It is flexible enough to capture complex dependency structures between events, while not sacrificing applicability in common scenarios. For anomaly detection, we develop methods that can detect new types of anomalies in continuous time and that show advantages compared to time discretization.
590 $a School code: 0178.
650 4 $a International conferences. $3 3558960
650 4 $a Artificial intelligence. $3 516317
650 4 $a Knowledge discovery. $3 3561807
650 4 $a Generalized linear models. $3 3561810
650 4 $a Signal processing. $3 533904
650 4 $a Information processing. $3 3561808
650 4 $a Informatics. $3 2142115
650 4 $a Time series. $3 3561811
653 $a Informatics
690 $a 0800
710 2 $a University of Pittsburgh. $3 958527
773 0 $t Dissertations Abstracts International $g 82-09B.
790 $a 0178
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28370948