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 is one of the most common methods for identifying and quantifying the relationship between a dependent variable and a single (univariate linear regression) or multiple (multivariate linear regression) independent variables. This model has a wide range of applications, from causal inference to predictive analysis and, in particular, time series forecasting.

The focus of this chapter is on methods and approaches for forecasting time series data with linear regression. That includes methods for decomposing and forecasting the series components (for example, the trend and seasonal patterns), handling special events (such as outliers and holidays), and using external variables as regressors.

This chapter covers the following topics:

  • Forecasting approaches with linear regression models
  • Extracting and estimating the series...