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morea/time-series-analysis/overview.md

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+## **HI-DSI WORKSHOP SYLLABUS: EXPLORATORY TIME-SERIES ANALYSIS IN R**
+
+**Duration**: Two (2) hours.
+
+**Objective**: This workshop provides an exciting journey into the realm of time series analysis using the R programming language. Participants will embark on a hands-on exploration of various techniques for understanding and visualizing time-dependent data. The workshop aims to equip participants with Exploratory Data Analysis (EDA) techniques for time series, empowering them to effectively employ this essential tool for a deeper comprehension of time series data prior to engaging in plotting or forecasting activities.
+
+**Tool Used**: R/R-Studio 
+
+**Prerequisites**: Basic understanding of statistics and data analysis concepts, and some familiarity with R/R-Studio.
+
+**Workshop Description**: This workshop will cover the fundamentals of descriptive models and methods for exploring characteristics of time series. Attendees will learn how to decompose and analyze a time series’ secular trend, seasonal, cyclical, and irregular variability components; create descriptive additive/multiplicative models of time series; and explore characteristics of stationarity, autocorrelation, and cross-correlation with other time series. The workshop aims to investigate the practical application of Exploratory Data Analysis (EDA) using NOAA/GML climate change data, including factors like Annual Surface Temperature Change, CO2 concentration, and the frequency of climate-related disasters. The session will involve hands-on EDA using the R programming language. 
+
+**Learning Objectives:**
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+
+
+1. Grasp the process of decomposing time series utilizing climate change data available on the R-Studio server during the workshop.
+2. Comprehend the exploratory techniques applied to time series analysis by breaking down climate change trends and variations to discern underlying causes and contributing factors..
+3. Comprehend exploratory methods for time series by breaking down trends and variations in climate change data to identify underlying causes and factors.
+4. Acquire practical experience using R as a tool for exploratory time-series analysis.
+5. Recognize the significance of exploratory data analysis in time series, acknowledging its pivotal role in achieving a more precise understanding of time series data.
+
+**Workshop Resources:**
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+
+
+* Download R: [https://cran.r-project.org/mirrors.html](https://cran.r-project.org/mirrors.html) 
+* Download R-Studio: [https://posit.co/download/rstudio-desktop/](https://posit.co/download/rstudio-desktop/) 
+* R Notebook: 
+* KOA Access: [https://koa.its.hawaii.edu/](https://koa.its.hawaii.edu/)
+* Presentation: [https://docs.google.com/presentation/d/1YLgclYe3Hkf8lIDctmLbreOeoq1R_nHEQ0Me4weTEMg/edit?usp=sharing](https://docs.google.com/presentation/d/1YLgclYe3Hkf8lIDctmLbreOeoq1R_nHEQ0Me4weTEMg/edit?usp=sharing)
+* Ice Breaker: [https://docs.google.com/presentation/d/13LXlBwBo1M4IRdF3K1uq5_U_FXInqP216wlHB8ESqcM/edit?usp=drive_link](https://docs.google.com/presentation/d/13LXlBwBo1M4IRdF3K1uq5_U_FXInqP216wlHB8ESqcM/edit?usp=drive_link)