Exploratory Time-Series Analysis in R
ABOUT EVENT
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 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.
Prerequisites
- 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
EVENT SPEAKERS
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Shidler College of Business, UHM
Dan Port
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