#### 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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# Lags analysis

The goal of lags analysis is to identify and quantify the relationship between a series and its lags. This relationship is typically measured by calculating the correlation between the two and with the use of data visualization tools. The level of correlation between a series and its lags is derived from the series characteristics. For instance, you should expect the series to have a strong correlation with its seasonal lags (for example, lags 12, 24, and 36 when the series frequency is monthly) when the series has strong seasonal patterns. This should make sense, as the direction of the series is impacted by its seasonal pattern. Another example is the price of a stock over time, which, in this case, should be correlated with the most recent lags. In the following examples, we will use the USgas, EURO_Brent, and USVSales series, each with different characteristics...