[KDD 2020] A Geometric Approach to Time Series Chains Improves Robustness - CrossMinds.ai
[KDD 2020] A Geometric Approach to Time Series Chains Improves Robustness
Aug 13, 20204 views
Makoto Imamura
 ,Time   series   motifs   have   become,   a   fundamental   tool   to   ,characterize repeated and conserved, structure, in systems, such as ,manufacturing  telemetry,,   economic  activities,,  and  both  ,human ,physiological,  and ,cultural,  behaviors,.  Recently  time  series  ,chains, ,were  introduced  as  a  generalization,  of  time  series  motifs  to  ,represent evolving patterns in time series, ,in order to characterize, ,the evolution of systems. Time series chains are a very promising ,primitive;, however,, we have observed ,that ,the original ,definition ,can be brittle, in the sense that ,a   small fluctuation in time series ,may ,“cut,”  a  chain.  Furthermore,  the  original  ,definition,  does  not  ,provide a measure of the “,significance,” of a chain, and therefore ,cannot ,support, top,-k search for chains or, provide a mechanism to ,discard spurious chains that might be discovered when searching ,large, datasets. ,Inspired by observations from dynamical systems ,theory,  t,his  paper  introduces  two,  novel,  quality ,metrics ,for,  time ,series chains, ,directionality, ,and ,graduality,,  to ,improve ,robustness, ,and ,to  enable  top,-K  search,.  With  extensive  empirical  work,  we ,show  that  our  proposed  definition,  is  much  more  robust  to  the  ,vagaries  of  real,-word  datasets ,and  allows  us  to  find  unexpected  ,regularities in time series datasets., ,
CCS CONCEPTS
, ,• Computing methodologies ,→ ,Similarity Detection; Motif,s. ,
KEYWORDS
, ,Time ,series,;  Time series m,otifs,;  Time series chains; ,Prognostics,  ,ACM Reference format:, ,Makoto  Imamura,  Takaaki  Nakamura,  and  Eamonn  Keogh.,  2020. ,Matrix ,Profile  XXI:  A  Geometric  Approach  to  Time  Series ,Chains  Improves  ,Robustness,.   In ,Proceedings   of   the   26th   ACM   SIGKDD   international   ,conference  on  Knowledge  d,iscovery  and  data  mining  (KDD’20). ,ACM, ,Virtual Event, CA,, USA, ,9 pages., ,hps://doi.org/10.1145/,3394486.3403164, , ,1 
SIGKDD_2020
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