TSNANNearestNeighborPropagation

TSNANNearestNeighborPropagation is a function that fills NA values with nearest real values in the past ( or future if the first position of time series is NA), for time series <code>X</code>.

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

TSNANNearestNeighborPropagation function

<dl><dt>X</dt>
<dd>is a T-by-D matrix numerical time series</dd></dl>

Arguments

TSNANNearestNeighborPropagation — TSNANNearestNeighborPropagation

<dl>

<dt>X</dt>
<dd>is a T-by-D matrix numerical time series</dd>

</dl>

TSNANNearestNeighborPropagation: TSNANNearestNeighborPropagation

Description

Usage

Value

Arguments

Examples