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 zoo class

The zoo package provides a framework for working with regular and irregular time series data. This includes the zoo class, an indexed object for storing time series data, and a set of functions for creating, preprocessing, and visualizing time series data. Similar to the ts and mts classes, the zoo class is comprised of two components:

  • Data structure: A vector (for univariate time series data) or matrix (for multivariance time series data) format
  • Index vector: This stores the series observation's corresponding index

On the other hand, unlike the ts class, the index of the zoo class has a flexible structure, as it can store different date and time classes, such as Date, POSIXct/lt, yearmon or yearqtr, as indices.

yearmon and yearqtr are two index classes for regular time series data. The yearmon class is suitable for representing a monthly time series when...