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Hands-On Time Series Analysis with R

Hands-On Time Series Analysis with R

By : Rami Krispin
3.8 (11)
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Hands-On Time Series Analysis with R

Hands-On Time Series Analysis with R

3.8 (11)
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)
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Decomposition of Time Series Data

Our primary focus in the previous chapters has been on the attributes and structure of time series data. Starting from this chapter, we are shifting gears and moving toward the analysis phase of time series data. This chapter focuses on one of the essential elements of time series analysis—the decomposition process of time series data to its components: the trend, seasonal, and random components. We will start with the moving average function and see its applications for smoothing time series data, removing seasonality, and estimating a series trend. In addition, we will introduce the decompose function and look at its applications. The topics in this chapter are an introduction to more advanced time series analysis topics that will be introduced later in the book.

In this chapter, we will cover the following topics:

  • The moving average...
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