Hands-On Time Series Analysis with R

More Information
Learn
  • Visualize time series data and derive better insights
  • Explore auto-correlation and master statistical techniques
  • Use time series analysis tools from the stats, TSstudio, and forecast packages
  • Explore and identify seasonal and correlation patterns
  • Work with different time series formats in R
  • Explore time series models such as ARIMA, Holt-Winters, and more
  • Evaluate high-performance forecasting solutions
About

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 foundations 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 and extract meaningful information from it using both descriptive statistics and rich data visualization tools in R such as the TSstudio, plotly, and ggplot2 packages. The later section of the book 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 cover 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 the skills needed to explore your data, identify patterns, and build a forecasting model using various traditional and machine learning methods.

Features
  • Perform time series analysis and forecasting using R packages such as Forecast and h2o
  • Develop models and find patterns to create visualizations using the TSstudio and plotly packages
  • Master statistics and implement time-series methods using examples mentioned
Page Count 448
Course Length 13 hours 26 minutes
ISBN 9781788629157
Date Of Publication 30 May 2019
Technical requirement
The linear regression
Forecasting with linear regression
Forecasting a series with multiseasonality components – a case study
Summary
Technical requirement
Why and when should we use machine learning?
Why h2o?
Forecasting monthly vehicle sales in the US – a case study
Summary

Authors

Rami Krispin

Rami Krispin is a data scientist at a major Silicon Valley company, where he focuses on time series analysis and forecasting. In his free time, he also develops open source tools and is the author of several R packages, including the TSstudio package for time series analysis and forecasting applications. Rami holds an MA in Applied Economics and an MS in actuarial mathematics from the University of Michigan—Ann Arbor.