Counting forbidden patterns in irregularly sampled time series. II. Reliability in the presence of highly irregular sampling.
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Counting forbidden patterns in irregularly sampled time series. I. The effects of under-sampling, random depletion, and timing jitter.Constructing ordinal partition transition networks from multivariate time seriesPrediction of flow dynamics using point processes.Using missing ordinal patterns to detect nonlinearity in time series data.
P2860
Counting forbidden patterns in irregularly sampled time series. II. Reliability in the presence of highly irregular sampling.
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2016 nî lūn-bûn
@nan
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
2016年论文
@zh
2016年论文
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name
Counting forbidden patterns in ...... of highly irregular sampling.
@en
Counting forbidden patterns in ...... of highly irregular sampling.
@nl
type
label
Counting forbidden patterns in ...... of highly irregular sampling.
@en
Counting forbidden patterns in ...... of highly irregular sampling.
@nl
prefLabel
Counting forbidden patterns in ...... of highly irregular sampling.
@en
Counting forbidden patterns in ...... of highly irregular sampling.
@nl
P2093
P2860
P356
P1433
P1476
Counting forbidden patterns in ...... of highly irregular sampling.
@en
P2093
Konstantinos Sakellariou
Michael McCullough
Thomas Stemler
P2860
P304
P356
10.1063/1.4970483
P577
2016-12-01T00:00:00Z