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)

Seasonality Analysis

Seasonality, as we saw in the previous chapter, is one of the main components of time series data. Furthermore, this component, when existing in a series, plays a pivotal role in the forecasting process of the future values of the series, as we will see in the coming chapters, since it contains structural patterns. In this chapter, we will focus on methods and approaches for identifying and then classifying the seasonal patterns of a series. This includes the use of descriptive statistics tools, such as summary statistics, as well as data visualization methods, utilizing packages such as dplyr, ggplot2, plotly, forecast, and TSstudio.

In this chapter, we will cover the following topics:

  • Single and multiple seasonality patterns
  • Descriptive statistic methods to identify seasonality patterns
  • Data visualization tools to explore and identify seasonality patterns...