Hands-On Time Series Analysis with R: Perform time series analysis and forecasting using R

by Rami Krispin

Build efficient forecasting models using traditional time series models and machine learning algorithms.

Key FeaturesPerform time series analysis and forecasting using R packages such as Forecast and h2oDevelop models and find patterns to create visualizations using the TSstudio and plotly packagesMaster statistics and implement time-series methods using examples mentionedBook DescriptionTime 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 foundations 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 and extract meaningful information from it using both descriptive statistics and rich data visualization tools in R such as the TSstudio, plotly, and ggplot2 packages. The later section of the book 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 cover 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 the skills needed to explore your data, identify patterns, and build a forecasting model using various traditional and machine learning methods.

What you will learnVisualize time series data and derive better insightsExplore auto-correlation and master statistical techniquesUse time series analysis tools from the stats, TSstudio, and forecast packagesExplore and identify seasonal and correlation patternsWork with different time series formats in RExplore time series models such as ARIMA, Holt-Winters, and moreEvaluate high-performance forecasting solutionsWho this book is forHands-On Time Series Analysis with R is ideal for data analysts, data scientists, and all R developers who are looking to perform time series analysis to predict outcomes effectively. A basic knowledge of statistics is required; some knowledge in R is expected, but not mandatory.

Table of ContentsIntroduction to Time Series Analysis and RWorking with Date and Time ObjectsThe Time Series ObjectWorking with zoo and xts ObjectsDecomposition of Time Series DataSeasonality AnalysisCorrelation AnalysisForecasting StrategiesForecasting with Linear RegressionForecasting with Exponential Smoothing ModelsForecasting with ARIMA ModelsForecasting with Machine Learning Models

762 pages, Kindle Edition

Published May 31, 2019

Reviews

[Murray Sondergard · Rating 3 out of 5]

The code and the data in the TSstudio package have changed since the book was published. As a result, the reader cannot reproduce all of the results that were in the book. In some cases, certain commands have been completely replaced, like the ts_acf() function. You have to hunt through the documentation to figure out what's going on. I certainly expected better.

[Jiwon Kim · Rating 4 out of 5]

For someone who is just learning, this is a great. Highly recommend.