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)

Historical background of time series analysis

Until recently, the use of time series data was mainly related to fields of science, such as economics, finance, physics, engineering, and astronomy. However, in recent years, as the ability to collect data improved with the use of digital devices such as computers, mobiles, sensors, or satellites, time series data is now everywhere. The enormous amount of data that's collected every day probably goes beyond our ability to observe, analyze, and understand it.

The development of time series analysis and forecasting did not start with the introduction of the stochastic process during the previous century. Ancient civilizations such as the Greeks, Romans, or Mayans researched and learned how to utilize cycled events such as weather, agriculture, and astronomy over time to forecast future events. For example, during the classic period of the Mayan civilization (between 250 AD and 900 AD), the Maya priesthood assumed that there are cycles in astronomy events and therefore they patiently observed, recorded, and learned those events. This allowed them to create a detailed time series table of past events, which eventually allowed them to forecast future events, such as the phases of the moon, eclipses of the moon and the sun, and the movement of stars such as Venus, Jupiter, Saturn, and Mars. The Mayan's priesthood used to collect data and analyze the data to identify patterns and cycles. This analysis was then utilized to predict future events. We can find a similarity between the Mayan's ancient analytical process and the time series analysis process we use now. However, the modern time series analysis process is based on statistical modeling and heavy calculations that are possible with today's computers and software, such as R.

Now that we defined the main characteristics of time series data, we can move forward and start to discuss the main characteristics of time series analysis.