Time Series Analysis with R

Time Series Analysis with R

Course Level: Intermediate (6 hours)

Predicting the future is a tough problem. Time series analysis makes it possible to assess whether or not predictions are possible and, if they are, build a model which can generate informed predictions for the future with realistic estimates of uncertainty. This training course will introduce participants to the packages in the Tidyverts.

The best qualification of a prophet is to have a good memory – George Savile

Book: Time Series Analysis with R

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Course Details

Outline

  • Introduction to tsibbles: Using the {tsibble} package to manipulate time series data
  • Features and Visualisation: Creating seasonal, lag and autocorrelation plots using the {feasts} package
  • STL Decomposition: De-constructing a time series into it’s seasonal and trend components
  • Introduction to forecasting: Constructing simple forecasts with the {fable} package
  • Exponential Smoothing: Creating and forecasting with ETS models
  • ARIMA models: Creating ARIMA models and forecasting

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Learning outcomes

Session 1:

By the end of session 1 participants will…

  • have an understanding of what time series are and be able to store time series data in R using {tsibble}.
  • know how to visualise time series data using {feast} and {ggplot2} for seasonal plots, subseries plots, lag plots and autocorrelation.
  • gain knowledge of time series decomposition and be able to fit and models using STL decomposition and plot the components. - have the ability to choose STL model parameters and be able to acquire seasonally adjusted series.

Session 2:

By the end of session 2 participants will…

  • be familiar with different forecasting methods.
  • be able to use the {fable} to create forecasts and {feasts} visualise them.
  • know how to extract prediction intervals from a forecast, get the residual forecast plots and determine accuracy of a forecast.
  • have an understanding of exponential smoothing for modelling time series.
  • be able to apply SES models.
  • have an understanding of ARIMA models and have the ability to fit, forecast and visualise both non-seasonal and seasonal ARIMA models.

This course does not include:

  • an in-depth discussion of the statistical principles behind the forecasting methods covered.
  • regression-based models - see our Tidymodels course instead.
  • spectral methods.

Prior knowledge

This course assumes basic familiarity with R and the {tidyverse}. Attending our Getting to Grips with the Tidyverse course, is more than sufficient in providing you with the prerequisite knowledge required for this course!

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Attendee Feedback

  • “Very beneficial course helped by insightful and clear explanations from the trainer.”
  • “This is my second course with Rhian and she is a great and clear instructor. She is engaging, even virtually, and I find the structure of the course very helpful for learning. Short lecture, followed by a demo, followed by a practical and then coming together to discuss is a great way to make the material stick. Thank you Rhian!”