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 linear regression

The primary usage of the linear regression model is to quantify the relationship between the dependent variable Y (also known as the response variable) and the independent variable/s X (also known as the predictor, driver, or regressor variables) in a linear manner. In other words, the model expresses the dependent variable as a linear combination of the independent variables. A linear relationship between the dependent and independent variables can be generalized by the following equations:

  • In the case of a single independent variable, the equation is as follows:
  • For n independent variables, the equation looks as follows:

The model variables for these equations are as follows:

  • i represents the observations index, i = 1,..., N
  • Yi represents the i observation of the dependent variable
  • Xj,i represents the i value of the j independent variable, where...