Once the data has been cleaned and reformatted, one of the first steps of the analysis is to identify the structure of the series components. The decomposition of time series is a generic name for the process of separating a series into its components. This process provides insights into the structural patterns of the series. Typically, those insights utilize and identify the most appropriate approaches to handle the series, based on the aim of the analysis (for example, seasonality analysis, and forecasting). For example, if you identify in this process that the series has a strong seasonality pattern, you should select models that have the ability to handle this pattern. Although there are multiple decomposition methods, in this chapter, we will focus on the classical seasonal decomposition method, as most methods are based on a type of extension...
Hands-On Time Series Analysis with R
By :
Hands-On Time Series Analysis with R
By:
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
Free Chapter
Introduction to Time Series Analysis and R
Working with Date and Time Objects
The Time Series Object
Working with zoo and xts Objects
Decomposition of Time Series Data
Seasonality Analysis
Correlation Analysis
Forecasting Strategies
Forecasting with Linear Regression
Forecasting with Exponential Smoothing Models
Forecasting with ARIMA Models
Forecasting with Machine Learning Models
Other Books You May Enjoy
How would like to rate this book
Customer Reviews
