Book Image

R for Data Science

By : Dan Toomey
Book Image

R for Data Science

By: Dan Toomey

Overview of this book

Table of Contents (19 chapters)

Chapter 11. Predicting Events with Machine Learning

R programming has several tools that can be used when dealing with events in a time series. We can look at the time series from several aspects, evaluate the components involved in the data, construct a model of the time series behavior, and estimate or forecast time series events going forward.

This chapter covers the analysis of time series data with the objective of forecasting. There are several areas in R programming that can be used for time series forecasting:

  • Converting your data into an R-formatted time series

  • Examining seasonality effects

  • Simple smoothing

  • Basic trend analysis, including decomposing your time series into seasonal, trend, and irregular components

  • Exponential smoothing, including Holt-Winters filtering, correlogram, and box test

  • ARIMA modeling