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)

Visualizing ts and mts objects

One of the first things that you probably want to do once you create or convert your data to a ts or mts format is to visualize it. This step is a quick sanity check to make sure that your data looks like you expect it would and to identify outliers, seasonality, and other patterns within your data. There are two approaches for visualizing a time series object:

  • Direct: This approach uses a visualization function to plot the object without any data transformation or conversion to another class. There are few packages that provide direct tools for visualizing time series objects. In this chapter, we will focus on three packages:
    • stats: In addition to the ts objects, this provides the plot.ts function for visualizing time series objects. This function is an extension of the plot function, which is an R built-in visualization function.
    • dygraphs: This...