Book Image

Hands-On Time Series Analysis with R

By : Rami Krispin
Book Image

Hands-On Time Series Analysis with R

By: Rami Krispin

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

Time series analysis is the art of extracting meaningful insights and revealing patterns from 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 goes through all the steps of the time series analysis process, from getting the raw data, to building a forecasting model using R. You will learn how to use tools from packages such as stats, lubridate, xts, and zoo to clean and reformat your raw data into structural time series data. As you make your way through Hands-On Time Series Analysis with R, 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 latter part of the book delves into traditional forecasting models such as time series regression models, exponential smoothing, and autoregressive integrated moving average (ARIMA) models using the forecast package. Last but not least, you will learn how to utilize machine learning models such as Random Forest and Gradient Boosting Machine to forecast time series data with the h2o package.