Paper Info
Title
Back to the future: changing the direction of time to discover causality
Abstract
In this paper we present the idea of using the direction of time to discover causality in temporal data. The Temporal Investigation Method for Enregistered Record Sequences (TIMERS), creates temporal classification rules from the input, and then measures the accuracy of the rules. It does so two times, each time assuming a different direction for time. The direction that results in rules with higher accuracy determines the nature of the relation. For causality, TIMERS assumes the natural direction of time, which assumes that events in the past can cause a target event in the present time. For the acausality test, TIMERS considers a backward flow of time, which assumes that events in the future can cause a target event in the present. There is a third alternative that TIMERS considers, and that is an instantaneous relation, where events in the present occur at the same time as a target event.
Year
DOI
Venue
2003
10.1007/3-540-44886-1_63
Canadian Conference on AI
Keywords
Field
DocType
different direction,target event,temporal investigation method,temporal data,enregistered record sequences,present time,instantaneous relation,natural direction,temporal classification rule,higher accuracy
Causality,Computer science,Theoretical computer science,Temporal database,Artificial intelligence,Timer,Distributed computing
Conference
Volume
ISSN
ISBN
2671
0302-9743
3-540-40300-0
Citations 
PageRank 
References 
0
0.34
1
Authors
1
Name
Order
Citations
PageRank
Kamran Karimi111817.23