Book Image

Hands-On Time Series Analysis with R

By : Rami Krispin
Book Image

Hands-On Time Series Analysis with R

By: Rami Krispin

Overview of this book

Time-series analysis is the art of extracting meaningful insights from, and revealing patterns in, time-series data using statistical and data visualization approaches. These insights and patterns can then be utilized to explore past events and forecast future values in the series. This book explores the basics of time-series analysis with R and lays the foundation you need to build forecasting models. You will learn how to preprocess raw time-series data and clean and manipulate data with packages such as stats, lubridate, xts, and zoo. You will analyze data using both descriptive statistics and rich data visualization tools in R including the TSstudio, plotly, and ggplot2 packages. The book then delves into traditional forecasting models such as time-series linear regression, exponential smoothing (Holt, Holt-Winter, and more) and Auto-Regressive Integrated Moving Average (ARIMA) models with the stats and forecast packages. You'll also work on advanced time-series regression models with machine learning algorithms such as random forest and Gradient Boosting Machine using the h2o package. By the end of this book, you will have developed the skills necessary for exploring your data, identifying patterns, and building a forecasting model using various traditional and machine learning methods.
Table of Contents (14 chapters)

xts, zoo, or ts – which one to use?

It depends.

There is no doubt that out of the three objects we have introduced so far (ts, zoo, and xts), the xts class is the most advanced and friendly to use. Moreover, since the xts class is also a zoo class with additional functionalities and improvements, the question actually should be xts or tswhich one to use? This mainly depends on the type of packages and applications you are using for time series analysis. However, in my mind, working with xts objects has more benefits compared to ts objects, since most of the forecasting models in R support only ts objects.

On the other hand, if you're not bound by requirements or if you just want to slice and dice a time series object, it is highly recommended that you use the xts (or zoo) object. The good news here, as we saw in some instances, is that both the xts and zoo...