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 Natural Gas Consumption dataset

In this chapter, and generally throughout this book, we will use the Natural Gas Consumption (NGC) dataset as an example of time series data. This dataset represents the quarterly consumption of natural gas in the US between 2000 and 2018. We will use the Quandl package to load the data from the Federal Reserve Bank of St. Louis database (FRED) and store it as a ts object:


NGC <-Quandl(code = "FRED/NATURALGAS",
type = "ts",
end_date = "2018-12-31")

The class of the output can be defined by the type argument, which, in this case, was set to the ts object:

## [1] "ts"

Typically, when loading a new dataset, it is recommended that you plot the series before moving to the next step in the analysis. This allows you...