#### 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.
Preface
Free Chapter
Introduction to Time Series Analysis and R
Working with Date and Time Objects
The Time Series Object
Working with zoo and xts Objects
Decomposition of Time Series Data
Seasonality Analysis
Correlation Analysis
Forecasting Strategies
Forecasting with Linear Regression
Forecasting with Exponential Smoothing Models
Forecasting with ARIMA Models
Forecasting with Machine Learning Models
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# Lag plots

A lag plot is a simplistic and non-statistical approach for analyzing the relationship between a series and its lags. As the name indicates, this method is based on data visualization tools, with the use of two-dimensional scatter plots for visualizing the series (typically on the y-axis) against the k lag of the series. Hence, each pair of points represents a combination of the series observations and their corresponding lagged values. As more points on the lag plot are closer to the 45 degree line, the higher the correlation will be between the series and the corresponding lag. The TSstudio package provides a customized function, ts_lags, for creating multiple lag plots. Let's use the function to plot the USgas series against its lags:

`ts_lags(USgas) `

We will get the following plot:

Looking at the lag plots of the USgas series, you can see that, moving along...