01 Dec 2023

Exploratory Time-Series Analysis in R



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 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 case study will explore NOAA/GML climate change data, such as Annual Surface Temperature Change, CO2 concentration, and Climate-related Disasters Frequency. Exploratory analysis will be conducted in R. The workshop will focus on Exploratory Data Analysis (EDA) over data wrangling and forecasting.


  • Basic statistics and familiarity with R.

Learning objectives

By the end of this workshop attendees will be able to:

  • 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

Tools Used for this workshop

  • R-Studio


  • Shidler College of Business, UHM

    Dan Port

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