2014 FBLGASimpleandEffectiveApproach

• (Cheng et al., 2014) ⇒ Dehua Cheng, Mohammad Taha Bahadori, and Yan Liu. (2014). “FBLG: A Simple and Effective Approach for Temporal Dependence Discovery from Time Series Data.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623709

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+FBLG%3A+A+Simple+and+Effective+Approach+for+Temporal+Dependence+Discovery+from+Time+Series+Data
• http://dl.acm.org/citation.cfm?id=2623330.2623709&preflayout=flat#citedby

Quotes

Author Keywords

• Generalized linear model; granger causality; time series analysis; time series analysis

Abstract

Discovering temporal dependence structure from multivariate time series has established its importance in many applications. We observe that when we look in reversed order of time, the temporal dependence structure of the time series is usually preserved after switching the roles of cause and effect. Inspired by this observation, we create a new time series by reversing the time stamps of original time series and combine both time series to improve the performance of temporal dependence recovery. We also provide theoretical justification for the proposed algorithm for several existing time series models. We test our approach on both synthetic and real world datasets. The experimental results confirm that this surprisingly simple approach is indeed effective under various circumstances.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 FBLGASimpleandEffectiveApproach	Yan Liu Mohammad Taha Bahadori Dehua Cheng			FBLG: A Simple and Effective Approach for Temporal Dependence Discovery from Time Series Data				10.1145/2623330.2623709		2014