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Hands-On Time Series Analysis with R

You're reading from   Hands-On Time Series Analysis with R Perform time series analysis and forecasting using R

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781788629157
Length 448 pages
Edition 1st Edition
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Author (1):
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Rami Krispin Rami Krispin
Author Profile Icon Rami Krispin
Rami Krispin
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Table of Contents (14) Chapters Close

Preface 1. Introduction to Time Series Analysis and R 2. Working with Date and Time Objects FREE CHAPTER 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

Seasonal analysis with descriptive statistics

Descriptive statistics are a simple yet powerful method to describe the key statistical characteristics of the data. This method is based on the use of summary statistics tables and is a summary of the key statistical indicators, such as the mean, median, quantile, and standard deviation, and data visualization tools, such as box plots and bar charts. Descriptive statistics can be used to describe the characteristics of the frequency units of a series. This allows us to identify whether we can segment each period of the series by some statistical criteria, for example, the mean, the quantile range, and so on.

The applications of the descriptive statistics methods are different from the ones of the statistical inference methods. While the first provides descriptions and insights on the data, the second offers conclusive insights about...
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