Algorithm to analyze nonlinear time series data
This package provides general tools for analyzing non-Gaussian nonlinear multivariate time series models. The algorithm is described in the paper Nonlinear Time Series Modeling by LPTime, Nonparametric Empirical Learning., by Mukhopadhyay and Parzen (2013). The central idea behind LPTime time series modelling algorithm is to convert the original univariate time series X(t) into
\mbox{Vec}(X)(t) = [\mbox{T}_{1}[X](t),…, \mbox{T}_{k}[X](t)]^{T}
via tailor-made orthonormal (mid-rank based) nonlinear transformation that automatically tackles heavy-tailed process (such as daily S&P 500 return data) by injecting robustness in the time series analysis, applicable for discrete and continuous time series data modelling.
The main functions are as follows: (1) LPTime; (2) LPiTrack
| Package: | LPTime |
| Type: | Package |
| Version: | 1.0-2 |
| Date: | 2015-03-03 |
| License: | GPL (>= 2) |
Subhadeep Mukhopadhyay, Shinjini Nandi
Maintainer: Shinjini Nandi <shinjini.nandi@temple.edu>
Mukhopadhyay, S. and Nandi, S. (2015). LPiTrack: Eye Movement Pattern Recognition Algorithm and Application to Biometric Identification.
Mukhopadhyay, S. and Parzen, E. (2014). LP approach to statistical modeling. arXiv:1405.2601.
Mukhopadhyay S. and Parzen E. (2013). Nonlinear Time Series Modeling by LPTime, Nonparametric Empirical Learning. arXiv:1308.0642.
Parzen E. and Mukhopadhyay S. (2013a). LP Mixed Data Science: Outline of Theory. arXiv:1311.0562.
Parzen, E. and Mukhopadhyay, S. (2012). Modeling, Dependence, Classification, United Statistical Science, Many Cultures. arXiv:1204.4699.
library(LPTime) data(EyeTrack.sample) head(LPiTrack(EyeTrack.sample), m = c(3, 5, 15), p = 3)
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