Forecasting and Time Series Models in R
Forecasting involves making predictions. It is required in many situations: deciding whether to build another power generation plant in the next ten years requires forecasts of future demand; scheduling staff in a call center next week requires forecasts of call volumes; stocking an 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 essential aid to effective and efficient planning. This course provides an introduction to time series forecasting using R.
At the completion of this course, you will be able to
Explore and visualize time series data.
Apply and interpret time series regression results.
Understand various methods to forecast time series data.
Use general forecasting tools and models for different forecasting situations.
Utilize statistical program to compute, visualize, and analyze time series data in economics, business, and the social sciences.
Introduction to the course and R
Introduction to the Course
Introduction to R: R environment, Working with the Rstudio IDE, Importing data in the Rstudio, Installing R packages, Using help, etc.
Time-series data analysis
Chapter 1: Getting started
Chapter 4: Judgmental forecasts
Chapter 10: Forecasting hierarchical or grouped time series [R scripts] [Notes]
Chapter 11: Advanced forecasting methods [R scripts] [Notes]
Chapter 12: Some practical forecasting issues [R scripts] [Notes]
Chapter 13: Other advanced methods [R scripts] [Notes]