From f293b07b30cef4446902da0b19fa21944e4675e5 Mon Sep 17 00:00:00 2001
From: Manos Katsomallos <manos@delkappa.com>
Date: Wed, 4 Aug 2021 01:07:03 +0300
Subject: [PATCH] correlation: Review

---
 text/preliminaries/correlation.tex | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/text/preliminaries/correlation.tex b/text/preliminaries/correlation.tex
index 7ccad56..a33f15b 100644
--- a/text/preliminaries/correlation.tex
+++ b/text/preliminaries/correlation.tex
@@ -7,7 +7,7 @@
 The most prominent types of correlation might be:
 
 \begin{itemize}
-  \item \emph{temporal}~\cite{wei2006time}---appearing in observations (i.e.,~values) of the same object over time.
+  \item \emph{Temporal}~\cite{wei2006time}---appearing in observations (i.e.,~values) of the same object over time.
   \item \emph{Spatial}~\cite{legendre1993spatial, anselin1995local}---denoted by the degree of similarity of nearby data points in space, and indicating if and how phenomena relate to the (broader) area where they take place.
   \item \emph{Spatiotemporal}---a combination of the previous categories, appearing when processing time series or sequences of human activities with geolocation characteristics, e.g.,~\cite{ghinita2009preventing}.
 \end{itemize}
@@ -20,7 +20,7 @@ A positive spatial autocorrelation indicates that similar data are \emph{cluster
 \subsection{Extraction of correlation}
 \label{subsec:cor-ext}
 
-A common practice for extracting data dependence from continuous data, is by expressing the data as a \emph{stochastic} or \emph{random process}.
+A common practice for extracting correlation from continuous data with dependence, is by expressing the data as a \emph{stochastic} or \emph{random process}.
 A random process is a collection of \emph{random variables} or \emph{bivariate data}, indexed by some set, e.g.,~a series of timestamps, a Cartesian plane $\mathbb{R}^2$, an $n$-dimensional Euclidean space, etc.~\cite{skorokhod2005basic}.
 The values a random variable can take are outcomes of an unpredictable process, while bivariate data are pairs of data values with a possible association between them.
 Expressing data as stochastic processes allows their modeling depending on their properties, and thereafter the discovery of relevant data dependence.