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)

Summary

In this chapter, we introduced two of the most advanced classes for time series data in R, the zoo and xts classes, and their applications. It is safe to claim that working with those objects, in particular, the xts class, is more friendly and convenient than the ts class. Their unique structure of data frames and well-organized time indices gives users seamless preprocessing and faster data querying. Furthermore, the zoo and xts packages have rich functionality and applications; more than we can cover in one chapter. Therefore, it is highly recommended that you look at the documentation and vignettes of the packages for more information.

In the next chapter, we will look at the decomposition of time series data process.