TY - GEN
T1 - Robust clustering for time series using spectral densities and functional data analysis
AU - Rivera-García, Diego
AU - García-Escudero, Luis Angel
AU - Mayo-Iscar, Agustín
AU - Ortega, Joaquín
N1 - Generated from Scopus record by KAUST IRTS on 2019-11-20
PY - 2017/1/1
Y1 - 2017/1/1
N2 - In this work a robust clustering algorithm for stationary time series is proposed. The algorithm is based on the use of estimated spectral densities, which are considered as functional data, as the basic characteristic of stationary time series for clustering purposes. A robust algorithm for functional data is then applied to the set of spectral densities. Trimming techniques and restrictions on the scatter within groups reduce the effect of noise in the data and help to prevent the identification of spurious clusters. The procedure is tested in a simulation study, and is also appliedtoarealdataset.
AB - In this work a robust clustering algorithm for stationary time series is proposed. The algorithm is based on the use of estimated spectral densities, which are considered as functional data, as the basic characteristic of stationary time series for clustering purposes. A robust algorithm for functional data is then applied to the set of spectral densities. Trimming techniques and restrictions on the scatter within groups reduce the effect of noise in the data and help to prevent the identification of spurious clusters. The procedure is tested in a simulation study, and is also appliedtoarealdataset.
UR - http://link.springer.com/10.1007/978-3-319-59147-6_13
UR - http://www.scopus.com/inward/record.url?scp=85020937651&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-59147-6_13
DO - 10.1007/978-3-319-59147-6_13
M3 - Conference contribution
SN - 9783319591469
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag [email protected]
ER -