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)

Summary

In this chapter, we introduced the ARIMA family of models, one of the core approaches for forecasting time series data. The main advantages of the ARIMA family of models is their flexibility and modularity, as they can handle both seasonal and non-seasonal time series data by adding or modifying the model components. In addition, we saw the applications of the ACF and PACF plots for identifying the type of process (for example, AR, MA, ARMA, and so on) and its order.

While it is essential to be familiar with the tuning process of ARIMA models, in practice, as the number series to be forecast increase, you may want to automate this process. The auto.arima function is one of the most common approaches in R to forecast with ARIMA models as it can scale up when dozens of series need to be forecast.

Last but not least, we saw applications of linear regression with the ARIMA...