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!
