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@@ -1342,3 +1342,30 @@ For additional {GBD} results and resources, visit the {GBD} 2019 Data Resources
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date = {2019},
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date = {2019},
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file = {background_female_reproductive_organs:/home/alex/Zotero/storage/5MAJ4CG5/background_female_reproductive_organs.png:image/png},
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file = {background_female_reproductive_organs:/home/alex/Zotero/storage/5MAJ4CG5/background_female_reproductive_organs.png:image/png},
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}
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}
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@book{hamilton_time_1994,
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location = {Princeton (N.J.)},
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title = {Time series analysis},
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isbn = {978-0-691-04289-3},
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publisher = {Princeton university press},
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author = {Hamilton, James Douglas},
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date = {1994},
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file = {PDF:/home/alex/Zotero/storage/J8GVKKHG/Hamilton - 1994 - Time series analysis.pdf:application/pdf},
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}
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@book{cryer_time_2008,
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location = {New York},
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edition = {2nd ed},
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title = {Time series analysis: with applications in R},
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isbn = {978-0-387-75958-6 978-0-387-75959-3},
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series = {Springer texts in statistics},
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shorttitle = {Time series analysis},
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pagetotal = {491},
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publisher = {Springer},
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author = {Cryer, Jonathan D. and Chan, Kung-sik},
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date = {2008},
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langid = {english},
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note = {{OCLC}: ocn191760003},
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keywords = {Data processing, R (Computer program language), Time-series analysis},
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file = {PDF:/home/alex/Zotero/storage/CIYMBUEW/Cryer and Chan - 2008 - Time series analysis with applications in R.pdf:application/pdf},
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}
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@@ -10,7 +10,6 @@
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\usepackage{graphicx}
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\usepackage{graphicx}
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\graphicspath{{resources/figures/}}
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\graphicspath{{resources/figures/}}
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\usepackage[inksacpeformat=png]{svg}
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\usepackage[style=ieee, backend=biber]{biblatex}
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\usepackage[style=ieee, backend=biber]{biblatex}
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\addbibresource{../main.bib}
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\addbibresource{../main.bib}
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@@ -89,7 +89,8 @@ improving accessibility and usability\cite{alexander_fertilitatsmonitoring_2014,
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Time series analysis is a fundamental tool for studying sequential data that evolves over time.
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Time series analysis is a fundamental tool for studying sequential data that evolves over time.
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Unlike other data types, time series data has an inherent temporal order, where each data point is associated
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Unlike other data types, time series data has an inherent temporal order, where each data point is associated
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with a timestamp, capturing its dependence on past values.
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with a timestamp, capturing its dependence on past values.
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The goal of time series analysis is to model these dependencies and extract meaningful insights.
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There are usually two main goals:
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While related to signal processing, time series analysis focuses more on long-term trends and forecasting,
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understanding the underlying mechanisms that lead to the observed data and predicting future data points based on the
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rather than frequency-domain characteristics.
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historical information and potentially external factors\cite{cryer_time_2008}
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\\
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\\
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Time series analysis encompasses various methods, ranging from simple statistical models to complex deep learning architectures.
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