Time Series Analysis with R

• 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

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.

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!