LPTime: Fits Vector Autoregressive model on LP transformed time...

In LPTime: LP Nonparametric Approach to Non-Gaussian Non-Linear Time Series Modelling

Description

Accepts possibly non-Gaussian non-linear univariate (stationary) time series data; converts it to multivariate LP-transformed series and fits a vector autoregressive (VAR) model.

Usage

LPTime(z, exo = NULL, m = 3, p = 10)

Arguments

z	Endogenous time series to be included in the VAR model.
exo	Exogenous time series to be included in the VAR model.
m	The number of required LP-transformations.
p	Lag-order of autoregression.

Details

LPTime algorithm models univariate stationary nonlinear process X(t) via linear modelling of the multivariate time series:

\mbox{Vec}(X)(t) = [\mbox{T}_{1}[X](t),…, \mbox{T}_{m}[X](t)]^{T},

where each of the time series components \mbox{T}_{j}[X](t) are polynomials of rank transformed X(t).

It fits vector autoregressive model of the form

\mbox{ Vec(T}[X])(t) = ∑_{k=1}^{p} A(k ; p)\, \mbox{Vec(T}[X])(t-k) \;+\; ε(t).

where ε(t) is multivariate mean zero Gaussian white noise with covariance Σ_{p}.

Value

A matrix of the estimated autoregressive coefficients obtained from LP-VAR model.

Author(s)

Shinjini Nandi

References

Mukhopadhyay, S. and Parzen, E. (2013). Nonlinear time series modeling by LPTime, nonparametric empirical learning. arXiv:1308.0642.

See Also

LPTrans, VAR

Examples

library(LPTime)
data(EyeTrack.sample)
head( LPTime(EyeTrack.sample, m = 2, p = 2))

LPTime documentation built on May 2, 2019, 7:18 a.m.