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)

The xts class

The eXtensible time series, xts package is an extension of the zoo package. It provides the xts class and a set of functions and tools for preprocessing, manipulating, and visualizing time series data. The xts class is a zoo object with additional attributes. Therefore, by default, any xts object carries zoo class attributes, and any of the zoo functions can be applied to the xts object. In the following examples, we will use the Michigan_CS series, which is an xts object that represents the famous consumer sentiment index of the University of Michigan since 1980. This series is available on the TSstudio package. We will start by loading the series and will review its main characteristics:


## The Michigan_CS series is a xts object with 1 variable and 468 observations
## Frequency: monthly
## Start time: Jan 1980
## End time...