The use of the series lags to forecast the future value of the series is beneficial whenever the series has stable repeated patterns over time. An excellent example of this type of series is the US natural gas consumption, as it has a strong seasonal pattern along with a consistent trend (or growth) pattern. Yet, the main pitfall of this method is that it will fail whenever the changes in the series derive from exogenous factors. In these cases, using only past lags could potentially lead to misleading results, as the lags do not necessarily drive the changes in the series. The goal of causality analysis, in the context of time series analysis, is to identify whether a causality relationship exists between the series we wish to forecast and other potential exogenous factors. The use of those external factors as drivers of the forecasting model (whenever exists...

#### Hands-On Time Series Analysis with R

##### By :

#### Hands-On Time Series Analysis with R

##### By:

#### 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)

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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