Learning Outcomes

Learn Exploratory Data Analysis of a Time Series in R

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 Outcomes

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. Acquire practical experience using R as a tool for exploratory time series analysis.
4. Recognize the significance of exploratory data analysis in time series, acknowledging its pivotal role in achieving a more precise understanding of time series data.

Primary Outcomes

• Understand the workflow for decomposing time-series using climate change data provided in the workshops R-Studio server.
• Understand key methods for additive/multiplicative models of time-series.
• Understand exploratory methods of time-series by decomposing climate change trends and variations to identify causes and factors.
• Experience with R as a tool for exploratory time-series analysis.
• Understand why exploratory data analysis of a time-series is a crucial tool for a more accurate understanding of data.

Secondary Outcomes

• Explore evidence of climate change via time series analysis
  • Climate change as evidenced by increasing global temperatures
  • Solar activity does not explain increasing global temperatures
  • A significant factor in increasing global temperatures is increasing CO2 concentrations in the atmosphere (greenhouse effect)
  • Increasing CO2 concentrations are due to human generated CO2 emissions
  • Increasing human generated CO2 emissions has increased frequency of climate disasters
  • What is the expected impact of decreasing human generated CO2 emissions

Readings

Experiential Learning

Assessments