TY - GEN
T1 - Granular representation schemes of time series
T2 - 2013 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
AU - Al-Hmouz, Rami
AU - Pedrycz, Witold
AU - Balamash, Abdullah
AU - Morfeq, Ali
PY - 2013
Y1 - 2013
N2 - Information granularity augments a variety of schemes of representation of time series, helps quantify the quality of models of the series and supports a thorough analysis of their parameters. This study introduces a concept of a granular representation of time series. We show that information granules formed on a basis of a given original numeric representation of the series can be optimized through a process of allocation (distribution) of information granularity being regarded here as an essential design asset. We formulate an optimization criterion and utilize a Particle Swarm Optimization (PSO) as an optimization vehicle to distribute a predefined level of information granularity. An optimization criterion used in the formation of the granular representation scheme is concerned with expressing and maximizing coverage of available temporal data by their granular representation. Experimental results in which we focus on the Piecewise Aggregate Approximation (PAA) offer details of the optimization of the allocation of granularity completed for some synthetic and real-world time series and quantify the performance of the resulting granular schemes of representation of time series.
AB - Information granularity augments a variety of schemes of representation of time series, helps quantify the quality of models of the series and supports a thorough analysis of their parameters. This study introduces a concept of a granular representation of time series. We show that information granules formed on a basis of a given original numeric representation of the series can be optimized through a process of allocation (distribution) of information granularity being regarded here as an essential design asset. We formulate an optimization criterion and utilize a Particle Swarm Optimization (PSO) as an optimization vehicle to distribute a predefined level of information granularity. An optimization criterion used in the formation of the granular representation scheme is concerned with expressing and maximizing coverage of available temporal data by their granular representation. Experimental results in which we focus on the Piecewise Aggregate Approximation (PAA) offer details of the optimization of the allocation of granularity completed for some synthetic and real-world time series and quantify the performance of the resulting granular schemes of representation of time series.
KW - allocation of information granularity
KW - Granular Computing
KW - Particle Swarm Optimization
KW - time series
UR - http://www.scopus.com/inward/record.url?scp=84885208484&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84885208484&partnerID=8YFLogxK
U2 - 10.1109/FOCI.2013.6602454
DO - 10.1109/FOCI.2013.6602454
M3 - Conference contribution
AN - SCOPUS:84885208484
SN - 9781467359016
T3 - Proceedings of the 2013 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
SP - 44
EP - 51
BT - Proceedings of the 2013 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Y2 - 16 April 2013 through 19 April 2013
ER -