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)

Seasonal analysis with descriptive statistics

Descriptive statistics are a simple yet powerful method to describe the key statistical characteristics of the data. This method is based on the use of summary statistics tables and is a summary of the key statistical indicators, such as the mean, median, quantile, and standard deviation, and data visualization tools, such as box plots and bar charts. Descriptive statistics can be used to describe the characteristics of the frequency units of a series. This allows us to identify whether we can segment each period of the series by some statistical criteria, for example, the mean, the quantile range, and so on.

The applications of the descriptive statistics methods are different from the ones of the statistical inference methods. While the first provides descriptions and insights on the data, the second offers conclusive insights about...