Change-HI/EDU

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:

  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: