research-article

Finding Periodic Discrete Events in Noisy Streams

Authors:

Christopher Lucas, and

Rik SarkarAuthors Info & Claims

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

November 2017

Pages 627 - 636

https://doi.org/10.1145/3132847.3132981

Published: 06 November 2017 Publication History

Abstract

Periodic phenomena are ubiquitous, but detecting and predicting periodic events can be difficult in noisy environments. We describe a model of periodic events that covers both idealized and realistic scenarios characterized by multiple kinds of noise. The model incorporates false-positive events and the possibility that the underlying period and phase of the events change over time. We then describe a particle filter that can efficiently and accurately estimate the parameters of the process generating periodic events intermingled with independent noise events. The system has a small memory footprint, and, unlike alternative methods, its computational complexity is constant in the number of events that have been observed. As a result, it can be applied in low-resource settings that require real-time performance over long periods of time. In experiments on real and simulated data we find that it outperforms existing methods in accuracy and can track changes in periodicity and other characteristics in dynamic event streams.

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Cited By

Zelenak HAtaee S(2022)Identifying Periodic Signal Patterns in Audio Streams2022 IEEE Western New York Image and Signal Processing Workshop (WNYISPW)10.1109/WNYISPW57858.2022.9983495(1-4)Online publication date: 4-Nov-2022
https://doi.org/10.1109/WNYISPW57858.2022.9983495
Shi TJi GYu ZZhao B(2021)Collective periodic pattern discovery for understanding human mobilityCluster Computing10.1007/s10586-020-03220-0Online publication date: 4-Jan-2021
https://doi.org/10.1007/s10586-020-03220-0
Toller MSantos TKern R(2019)SAZED: parameter-free domain-agnostic season length estimation in time series dataData Mining and Knowledge Discovery10.1007/s10618-019-00645-zOnline publication date: 26-Jul-2019
https://doi.org/10.1007/s10618-019-00645-z

Index Terms

Finding Periodic Discrete Events in Noisy Streams
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Data stream mining

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cover image ACM Conferences

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

November 2017

2604 pages

ISBN:9781450349185

DOI:10.1145/3132847

General Chairs:
Ee-Peng Lim
Singapore Management University, Singapore
,
Marianne Winslett
University of Illinois at Urbana-Champaign, USA, and Advanced Digital Sciences Center, Singapore
,
Program Chairs:
Mark Sanderson
RMIT, Australia
,
Ada Fu
Chinese University of Hong Kong, Hong Kong
,
Jimeng Sun
Georgia Tech, USA
,
Shane Culpepper
RMIT, Australia
,
Eric Lo
Chinese University of Hong Kong, Hong Kong
,
Joyce Ho
Emory University, USA
,
Debora Donato
Mix Tech, Inc., USA
,
Rakesh Agrawal
Data Insights Laboratories, USA
,
Yu Zheng
Microsoft Research Asia, China
,
Carlos Castillo
Qatar Computing Research Institute, Qatar
,
Aixin Sun
Nanyang Technological University, Singapore
,
Vincent S. Tseng
National Cheng Kung University, Taiwan
,
Chenliang Li
Wuhan University, China

Copyright © 2017 ACM.

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Published: 06 November 2017

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November 6 - 10, 2017

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Cited By

Zelenak HAtaee S(2022)Identifying Periodic Signal Patterns in Audio Streams2022 IEEE Western New York Image and Signal Processing Workshop (WNYISPW)10.1109/WNYISPW57858.2022.9983495(1-4)Online publication date: 4-Nov-2022
https://doi.org/10.1109/WNYISPW57858.2022.9983495
Shi TJi GYu ZZhao B(2021)Collective periodic pattern discovery for understanding human mobilityCluster Computing10.1007/s10586-020-03220-0Online publication date: 4-Jan-2021
https://doi.org/10.1007/s10586-020-03220-0
Toller MSantos TKern R(2019)SAZED: parameter-free domain-agnostic season length estimation in time series dataData Mining and Knowledge Discovery10.1007/s10618-019-00645-zOnline publication date: 26-Jul-2019
https://doi.org/10.1007/s10618-019-00645-z

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