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)

Forecasting monthly vehicle sales in the US – a case study

In this chapter, we will focus on forecasting the total monthly vehicle sales in the US in the next 12 months with ML methods. The total monthly vehicle sales in the US series is available in the TSstudio package:



Before we start preparing the series and create new features, we will conduct a short exploratory analysis of the series in order to identify the main characteristics of the series.

Exploratory analysis of the USVSales series

In this section, we will focus on exploring and learning about the main characteristics of the series. These insights will be used to build new features as inputs for the ML model. The exploratory...