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Hands-On Time Series Analysis with R

You're reading from Hands-On Time Series Analysis with R

Product type Book

Published in May 2019

Publisher Packt

ISBN-13 9781788629157

Pages 448 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Rami Krispin

Table of Contents (14) Chapters

Preface

1. Introduction to Time Series Analysis and R

2. Working with Date and Time Objects

3. The Time Series Object

4. Working with zoo and xts Objects

5. Decomposition of Time Series Data

6. Seasonality Analysis

7. Correlation Analysis

8. Forecasting Strategies

9. Forecasting with Linear Regression

10. Forecasting with Exponential Smoothing Models

11. Forecasting with ARIMA Models

12. Forecasting with Machine Learning Models

13. Other Books You May Enjoy

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Lags analysis

The goal of lags analysis is to identify and quantify the relationship between a series and its lags. This relationship is typically measured by calculating the correlation between the two and with the use of data visualization tools. The level of correlation between a series and its lags is derived from the series characteristics. For instance, you should expect the series to have a strong correlation with its seasonal lags (for example, lags 12, 24, and 36 when the series frequency is monthly) when the series has strong seasonal patterns. This should make sense, as the direction of the series is impacted by its seasonal pattern. Another example is the price of a stock over time, which, in this case, should be correlated with the most recent lags. In the following examples, we will use the USgas, EURO_Brent, and USVSales series, each with different characteristics...

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Authors (1)

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 MichiganAnn Arbor.

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