plotTimeSeries

plotTimeSeries is a function for visualizing time series

A framework to infer causality on a pair of time series of real numbers based on variable-lag Granger causality and transfer entropy. Typically, Granger causality and transfer entropy have an assumption of a fixed and constant time delay between the cause and effect. However, for a non-stationary time series, this assumption is not true. For example, considering two time series of velocity of person A and person B where B follows A. At some time, B stops tying his shoes, then running to catch up A. The fixed-lag assumption is not true in this case. We propose a framework that allows variable-lags between cause and effect in Granger causality and transfer entropy to allow them to deal with variable-lag non-stationary time series. Please see Chainarong Amornbunchornvej, Elena Zheleva, and Tanya Berger-Wolf (2021) <doi:10.1145/3441452> when referring to this package in publications.

Chainarong Amornbunchornvej

VLTimeCausality

Variable-Lag Time Series Causality Inference Framework

plotTimeSeries function

<dl><dt>X</dt>
<dd>is a 1st numerical time series</dd>
<dt>Y</dt>
<dd>is a 2nd numerical time series. If it is not supplied, the function plots only <code>X</code>.</dd>
<dt>strTitle</dt>
<dd>is a string of the plot title</dd>
<dt>TSnames</dt>
<dd>is a list of legend of <code>X,Y</code> where TSnames[1] is a legend of <code>X</code> and TSnames[2] is a legend of <code>Y</code>.</dd></dl>

Arguments

plotTimeSeries — plotTimeSeries

<dl>

<dt>X</dt>
<dd>is a 1st numerical time series</dd>


<dt>Y</dt>
<dd>is a 2nd numerical time series. If it is not supplied, the function plots only <code>X</code>.</dd>


<dt>strTitle</dt>
<dd>is a string of the plot title</dd>


<dt>TSnames</dt>
<dd>is a list of legend of <code>X,Y</code> where TSnames[1] is a legend of <code>X</code> and TSnames[2] is a legend of <code>Y</code>.</dd>

</dl>

plotTimeSeries: plotTimeSeries

Description

Usage

Value

Arguments

Examples