#### 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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# The moving average function

The moving average (MA) is a simple function for smoothing time series data. This function is based on averaging each observation of a series, when applicable, with its surrounding observations, that is, with a past, future, or a combination of both past and future observations, in chronological order. The output of this transformation process is a smoothed version of the original series. The MA function has a variety of applications, such as data smoothing, noise reduction, and trend estimation. Also, with some small modifications, this function can be used as a forecasting model. In Chapter 10, Forecasting with Exponential Smoothing Models, we will discuss the forecasting applications of the MA function in detail. The main components of the MA function are as follows:

• The rolling window: This is a generic function that slides across the series in...