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)


The decomposition of time series data down to its components is one of the core methods in time series analysis. The use of this method is part of descriptive analysis, as it can provide some useful insights into series patterns and structures. Those insights can be utilized to identify the best approaches and models to be used with a series.

The focus of this chapter has been on the classical seasonal decomposition process with MA, one of the most common decomposition methods. Although this method is not the most advanced or accurate, it is the basis of most advanced methods. Therefore, understanding the mechanisms of this process, such as the role of the MA, means that you can apply more sophisticated methods with minimum effort.

In the next chapter, we will focus on the analysis of the seasonal component of time series data.