R Cookbook, 2nd Edition

Chapter 14. Time Series Analysis

Time series analysis has become a hot topic with the rise of quantitative finance and automated trading of securities. Many of the facilities described in this chapter were invented by practitioners and researchers in finance, securities trading, and portfolio management.

Before you start any time series analysis in R, a key decision is your choice of data representation (object class). This is especially critical in an object-oriented language such as R, because the choice affects more than how the data is stored; it also dictates which functions (methods) will be available for loading, processing, analyzing, printing, and plotting your data. When many people start using R they simply store time series data in vectors. That seems natural. However, they quickly discover that none of the coolest analytics for time series analysis work with simple vectors. We’ve found when users switch to using an object class intended for time series data, the analysis gets easier, opening a gateway to valuable functions and analytics.

This chapter’s first recipe recommends using the zoo or xts packages for representing time series data. They are quite general and should meet the needs of most users. Nearly every subsequent recipe assumes you are using one of those two representations.

Note

The xts implementation is a superset of zoo, so xts can do everything that zoo can do. In this chapter, whenever a recipe works for a zoo object, you can safely assume (unless ...