diff --git a/main.bib b/main.bib
index c25c84c..ec36d7f 100644
--- a/main.bib
+++ b/main.bib
@@ -1342,3 +1342,30 @@ For additional {GBD} results and resources, visit the {GBD} 2019 Data Resources
 	date = {2019},
 	file = {background_female_reproductive_organs:/home/alex/Zotero/storage/5MAJ4CG5/background_female_reproductive_organs.png:image/png},
 }
+
+@book{hamilton_time_1994,
+	location = {Princeton (N.J.)},
+	title = {Time series analysis},
+	isbn = {978-0-691-04289-3},
+	publisher = {Princeton university press},
+	author = {Hamilton, James Douglas},
+	date = {1994},
+	file = {PDF:/home/alex/Zotero/storage/J8GVKKHG/Hamilton - 1994 - Time series analysis.pdf:application/pdf},
+}
+
+@book{cryer_time_2008,
+	location = {New York},
+	edition = {2nd ed},
+	title = {Time series analysis: with applications in R},
+	isbn = {978-0-387-75958-6 978-0-387-75959-3},
+	series = {Springer texts in statistics},
+	shorttitle = {Time series analysis},
+	pagetotal = {491},
+	publisher = {Springer},
+	author = {Cryer, Jonathan D. and Chan, Kung-sik},
+	date = {2008},
+	langid = {english},
+	note = {{OCLC}: ocn191760003},
+	keywords = {Data processing, R (Computer program language), Time-series analysis},
+	file = {PDF:/home/alex/Zotero/storage/CIYMBUEW/Cryer and Chan - 2008 - Time series analysis with applications in R.pdf:application/pdf},
+}
diff --git a/thesis/main.tex b/thesis/main.tex
index fb24e77..96cb820 100644
--- a/thesis/main.tex
+++ b/thesis/main.tex
@@ -10,7 +10,6 @@
 
 \usepackage{graphicx}
 \graphicspath{{resources/figures/}}
-\usepackage[inksacpeformat=png]{svg}
 
 \usepackage[style=ieee, backend=biber]{biblatex}
 \addbibresource{../main.bib}
diff --git a/thesis/sections/background.tex b/thesis/sections/background.tex
index 87b93b4..126152f 100644
--- a/thesis/sections/background.tex
+++ b/thesis/sections/background.tex
@@ -89,7 +89,8 @@ improving accessibility and usability\cite{alexander_fertilitatsmonitoring_2014,
 Time series analysis is a fundamental tool for studying sequential data that evolves over time.
 Unlike other data types, time series data has an inherent temporal order, where each data point is associated
 with a timestamp, capturing its dependence on past values.
-The goal of time series analysis is to model these dependencies and extract meaningful insights.
-While related to signal processing, time series analysis focuses more on long-term trends and forecasting,
-rather than frequency-domain characteristics.
+There are usually two main goals:
+understanding the underlying mechanisms that lead to the observed data and predicting future data points based on the
+historical information and potentially external factors\cite{cryer_time_2008}
 \\
+Time series analysis encompasses various methods, ranging from simple statistical models to complex deep learning architectures.
\ No newline at end of file