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Multivariate polynomial regression for identification of chaotic time series
D. A. Vaccari
, H. K. Wang
Department of Civil, Environmental and Ocean Engineering
Cisco Systems
Research output
:
Contribution to journal
›
Article
›
peer-review
13
Scopus citations
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Earth and Planetary Sciences
Polynomial
100%
Regression
100%
Time Series
100%
Model
75%
Term
50%
Glass
50%
Simulation
25%
Proving
25%
Autocorrelation
25%
Approach
25%
Calculation
25%
Datum
25%
Geometry
25%
Dimension
25%
Tendency
25%
Data Set
25%
Physics
Model
100%
Behavior
66%
Terms
66%
Glass
66%
Simulation
33%
Dimensions
33%
Autocorrelation
33%
Calculation
33%
Geometry
33%
Lyapunov Exponent
33%
Non-Linear Dynamic
33%
Noise Measurement
33%
Computer Science
Models
100%
Identification
100%
Polynomial Regression
100%
Simulation
33%
Validation
33%
Embedding
33%
Measurement Noise
33%
Dynamic Behavior
33%
Time Series Data
33%
Lyapunov Exponent
33%
Mathematics
Nonlinear
50%
Lyapunov Exponent
50%
Polynomial
50%
Terms
50%
Time Series Data
50%
Geometry
50%
Validation
50%
Economics, Econometrics and Finance
Time Series
100%
Polynomial Regression
100%