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)

Forecasting with linear regression

The linear regression model, unlike the traditional time series models such as the ARIMA or Holt-Winters, was not designed explicitly to handle and forecast time series data. Instead, it is a generic model with a wide range of applications from causal inference to predictive analysis.

Therefore, forecasting with a linear regression model is mainly based on the following two steps:

  1. Identifying the series structure, key characteristics, patterns, outliers, and other features
  2. Transforming those features into input variables and regressing them with the series to create a forecasting model

The core features of a linear regression forecasting model are the trend and seasonal components. The next section focuses on identifying the series trend and seasonal components and then transforming them into input variables of the regression model.