Work

AutoCyclone

Year: 2017

Type: conference-paper

Abstract: Given a collection of seasonal time-series, how can we find regular (cyclic) patterns and outliers (i.e. rare events)? These two types of patterns are hidden and mixed in the time-varying activities. How can we robustly separate regular patterns and outliers, without requiring any prior information?

Source: International World Wide Web Conference (WWW)

DOI: 10.1145/3038912.3052595

Authors: Tsubasa Takahashi, Bryan Hooi, Christos Faloutsos

Institutions: NEC (Japan), Carnegie Mellon University

Language: English

Topic: Time Series Analysis and Forecasting

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, Army Research Laboratory

Awards: TWC: Medium: Collaborative: Know Thy Enemy: Data Mining Meets Networks for Understanding Web-Based Malware Dissemination, III: Medium: Collaborative Research: Collective Opinion Fraud Detection: Identifying and Integrating Cues from Language, Behavior, and Networks, BIGDATA: Mid-Scale: DA: Collaborative Research: Big Tensor Mining: Theory, Scalable Algorithms and Applications