# colorblind friendly palette palette(c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")) library('knitr') knitr::opts_chunk$set(fig.width=7,fig.height=4.7,fig.show='hold')

The **nonlinearTseries** package provides functionality for nonlinear
time series analysis. This package permits the computation of the most-used
nonlinear statistics/algorithms including generalized correlation dimension,
information dimension, largest Lyapunov exponent, sample entropy and
Recurrence Quantification Analysis (RQA), among others. Basic routines
for surrogate data testing are also included. This vignette provides a brief
overview of the most important routines contained in **nonlinearTseries**.

To explore the routines included in **nonlinearTseries**, we will study the
famous Lorenz system.
**nonlinearTseries** offers different routines for simulating the best well-known
nonlinear systems:

suppressMessages(library('nonlinearTseries')) library('plot3D') # by default, the simulation creates a RGL plot of the system's phase space lor = lorenz(do.plot = F) # let's plot the phase space of the simulated lorenz system scatter3D(lor$x, lor$y, lor$z, main = "Lorenz's system phase space", col = 1, type="o",cex = 0.3)

It must be noted that the `lorenz`

function returns the simulated components
of the system in a `list`

. Future versions of the package will allow to obtain
the same simulations as `ts`

objects.

A complete list of the available functions for nonlinear systems simulation
can be found in the `lorenz`

help page (`?lorenz`

command).

Usually, what we observe in a physical experiment is a single time series and not the complete phase space. For example, let's assume that we have only measured the $x$ component of the Lorenz system. Fortunately, we can still infer the properties of the phase space by constructing a set of vectors whose components are time delayed versions of the $x$ signal $[x(t), x(t+\tau), ..., x(t + m\tau)]$ (This theoretical result is referred to as the Takens' embedding theorem).

The **nonlinearTseries** package provides functions for estimating proper values
of the embedding dimension $m$ and the delay-parameter $\tau$. First, the
delay-parameter can be estimated by using the autocorrelation function or the
average mutual information of the signal.

# suppose that we have only measured the x-component of the Lorenz system lor.x = lor$x old.par = par(mfrow = c(1, 2)) # tau-delay estimation based on the autocorrelation function tau.acf = timeLag(lor.x, technique = "acf", lag.max = 100, do.plot = T) # tau-delay estimation based on the mutual information function tau.ami = timeLag(lor.x, technique = "ami", lag.max = 100, do.plot = T) par(old.par)

Both techniques select a time-lag based on the behavior of the autocorrelation or the average mutual information function. Since the autocorrelation function is a linear statistic we usually obtain more appropriate values with the mutual information technique. Thus, for the remainder of this section, we will use the value obtained with this technique.

Once the time-lag parameter has been estimated, a proper embedding dimension can
be computed by using the well-known Cao's algorithm (see the documentation
of the `estimateEmbeddingDim`

function for references):

emb.dim = estimateEmbeddingDim(lor.x, time.lag = tau.ami, max.embedding.dim = 15)

When applied to the Lorenz system, the Cao's algorithm suggests the use of
an embedding dimension of 4. The final phase space reconstruction can be obtained
using the `buildTakens`

function:

tak = buildTakens(lor.x,embedding.dim = emb.dim, time.lag = tau.ami) scatter3D(tak[,1], tak[,2], tak[,3], main = "Lorenz's system reconstructed phase space", col = 1, type="o",cex = 0.3)

Note that the reconstructed and the original phase space, although different, share similar topological features.

In practical applications, some of the best well-known nonlinear statistics (such as the Lyapunov exponent, the generalized correlation dimensions or the sample entropies) share a similar estimation process. This process could be summarized as follows:

- Perform some heavy computations characterizing
either the scaling behavior of the attractor in the phase space
(e.g. correlation dimension) or the dynamical evolution of the system in time

(e.g. Lyapunov exponent). These computations are usually repeated for several embedding dimensions. - The estimation of the nonlinear statistic requires the existence of a
small region (in space or time) in which the function computed in the previous
step manifests a linear behavior. The slope of this linear region yields the
value of the nonlinear statistic. Since the nonlinear statistics in which we
are interested in are invariants of the dynamical system, their values should not
depend on the embedding dimension used to estimate them (provided that we are using an
embedding dimension large enough to reconstruct the phase space). Thus, it is
important to check for the existence of this linear region through plots. We
should also check that the slope of this region does not depend on the embedding
dimension. The
`plot`

function can be used with all the objects involved in the computation of these statistics. - Once the linear-region has been localized, the nonlinear statistic
is obtained by performing a linear regression using the
`estimate`

function.

In the following sections we illustrate this procedure computing the correlation dimension, the sample entropy and the Lyapunov exponents of the Lorenz system.

The correlation dimension is a technique that measures the fractal dimension of
the phase space of a dynamical system. To verify
that the estimation of the correlation dimension does not depend on the
embedding dimension, we compute the correlation sums (`corrDim`

function)
for several embedding dimensions. Once we have checked for the existence of the
linear regions in different embedding dimensions, we obtain an estimation of
the correlation dimension with the `estimate`

function. This function allows to
specify the range in which the linear behavior appears (`regression.range`

parameter) as well as the embedding dimensions
to be used for the estimation of the correlation dimension (`use.embeddings`

parameter). The final estimation of the correlation dimension is an average of
the slopes obtained for each embedding dimension.

cd = corrDim(lor.x, min.embedding.dim = emb.dim, max.embedding.dim = emb.dim + 5, time.lag = tau.ami, min.radius = 0.001, max.radius = 50, n.points.radius = 40, do.plot=FALSE) plot(cd) cd.est = estimate(cd, regression.range=c(0.75,3), use.embeddings = 5:7) cat("expected: 2.05 --- estimate: ",cd.est,"\n")

The generalized correlation dimensions can also be computed with the `corrDim`

function (by modifying the `q`

parameter). To estimate the information dimension,
**nonlinearTseries** provides the `infDim`

function (see `?infDim`

for more
information).

The sample entropy is a technique for measuring the unpredictability of a time
series. It is possible to use the correlation sums for obtaining an estimation
of the sample entropy of a time series. In this case, the computations should
yield a function with a clear plateau. The value of this plateau is an estimation
of the sample entropy. The next chunk of code illustrates the procedure for
estimating the sample entropy from a previously computed `corrDim`

object.

se = sampleEntropy(cd, do.plot = F) se.est = estimate(se, do.plot = F, regression.range = c(8,15)) cat("Sample entropy estimate: ", mean(se.est), "\n")

One of the more important characteristics of a chaotic system is its sensitivity
to initial conditions. As a consequence of this sensitivity, close trajectories
diverge exponentially fast. The maximum Lyapunov exponent measures the
average rate of divergence of close trajectories in the system. The `maxLyapunov`

function can be used for computing this divergence through time. To define what is
a close trajectory we make use of the `radius`

parameter. After the computation of the divergence rates we can get an
estimate of the maximum Lyapunov exponent by performing a linear regression
(`estimate`

function), just as we did with the correlation dimension.

# get the sampling period of the lorenz simulation # computing the differences of time (all differences should be equal) sampling.period = diff(lor$time)[1] ml = maxLyapunov(lor.x, sampling.period=0.01, min.embedding.dim = emb.dim, max.embedding.dim = emb.dim + 3, time.lag = tau.ami, radius=1, max.time.steps=1000, do.plot=FALSE) plot(ml,type="l", xlim = c(0,8)) ml.est = estimate(ml, regression.range = c(0,3), do.plot = T,type="l") cat("expected: 0.906 --- estimate: ", ml.est,"\n")

Although we have postponed its discussion until the end of this vignette, the first step before studying a system using nonlinear analysis techniques should be checking that the data shows indeed some degree of nonlinearity.

The preferred method for nonlinearity-test in literature is surrogate data testing. In surrogate data testing, a statistic $\mu$ quantifying some nonlinear feature of the data is computed and compared with the resulting values for an ensemble of comparable linear processes.

**nonlinearTseries** includes basic functionality for surrogate data testing. The
next example performs surrogate data testing by measuring the time
asymmetry of the data and the surrogates (since linear stochastic processes are
symmetric under time reversal, a deviation from the distribution of the surrogates
would be a strong sign of nonlinearity). From the resulting figure, it is clear
that our time series shows some degree of nonlinearity.

st = surrogateTest(lor.x,significance = 0.05,one.sided = F, FUN = timeAsymmetry, do.plot=F) plot(st)

In this quickstart vignette we have only covered some of the main functions included
in **nonlinearTseries**. Other interesting functions included in this package are
enumerated below. The main reason for not including them in this quickstart guide
is that these functions are quite simple to use.

`rqa`

: performs Recurrence Quantification Analysis.`dfa`

: performs Detrended Fluctuation Analysis.`nonLinearNoiseReduction`

: denoises a given time series using phase space techniques.`poincareMap`

: computes a Poincare map of the trajectories in phase space.`spaceTimePlot`

: shows the space time separation plot: broadly-used method of detecting non-stationarity and temporal correlations in time series.

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