Time Series Analysis Using R

Course 1269

  • Duration: 1 day
  • Language: English
  • Level: Foundation

A time series is simply a series of data points arranged chronologically.

Time series appear in many contexts.

  • Finance (e.g. stock prices)
  • Marketing (e.g. new customers, sales)
  • Health (e.g. infections, deaths)
  • Science (e.g earthquakes)
  • ...and many, many more

Time Series Analysis Delivery Method

  • In-Person

  • Online

Time Series Analysis Course Information

In this course, you will:

  • Work with the R statistical modeling language to analyse data.
  • Import internal and external data sources into R.
  • Work with time-series data.
  • Visualise time-series data.
  • Perform automated forecasting using Prophet.
  • Consider the potential impact of machine learning on time series analysis.
  • Leverage continued support with after-course one-on-one instructor coaching and computing sandbox.



Time Series Analysis Instructor-Led Course Outline

  • Basics of writing R scripts
  • Viewing data sets in R
  • Querying and Saving Data in R
  • Exploratory Data Analysis
  • Visualising Data
  • Transforming Data
  • Time Series in R
  • Creating Time Series
  • Querying and Data Manipulation
  • Combining Time Series
  • Rate of Change
  • Applying Functions to Intervals
  • Plotting Basics
  • Univariate Time Series
  • Multivariate Time Series
  • Generate Forecasts using Prophet
  • Forecast COVID-19 Cases

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Time Series Analysis FAQs

No. You will be taught basic R skills as part of the course.

This is an introductory course. If you have a favorite time series technique, then you are possibly already beyond this course. The focus is very much on the basics. We make extensive use of the popular `xts` package and introduce Meta's (Facebook's) Prophet library.

The focus of the course is on time series analysis. R is merely used as a tool to that end. So, yes---the concepts will translate to your preferred environment.

Yes. There are various opportunities to apply the ideas presented to real-world time series data.