Learn time series and forecasting with the creator of the forecast package, Rob Hyndman.

Forecasting is required in many situations: deciding whether to build another power generation plant in the next five years requires forecasts of future demand; scheduling staff in a call centre next week requires forecasts of call volume; stocking inventory requires forecasts of stock requirements. Forecasts can be required several years in advance (for the case of capital investments), or only a few minutes beforehand (for telecommunication routing). Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning.

In this three-day workshop, we will explore methods and models for forecasting time series. Topics to be covered include seasonality and trends, exponential smoothing, ARIMA modelling, dynamic regression and hierarchical forecasting, as well as forecast accuracy methods and forecast evaluation techniques such as cross-validation.

Course participants will be assumed to be familiar with basic statistical tools such as multiple regression, but no knowledge of time series or forecasting will be assumed. Some prior experience in R is required. The DataCamp course Introduction to R would be suitable preparation for those who have not previously used R.

Day 1: Time series graphics, benchmark forecasting methods, forecast evaluation, seasonality and trends, exponential smoothing.

Day 2: ETS models, transformations and differencing, ARIMA models

Day 3: Dynamic regression, forecasting with multiple seasonality, hierarchical forecasting

Requirements

Participants must bring their own laptop with a recent version of R and RStudio installed, along with the following packages and their dependencies:

• fpp2
• readxl
• thief
• knitr

Reference

Hyndman and Athananasopoulos (2018) Forecasting: Principles and Practice, 2nd ed., OTexts. http://otexts.org/fpp2/

For more information please contact us.