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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

Forecasting with moving average models

In Chapter 5, Decomposition of Time Series Data, we looked at the application of the moving average functions to smooth time series data. Those functions, with a small tweak, can be used as a forecasting model. In the upcoming section, we will introduce two of the most common moving average forecasting functions—the simple and weighted moving average. These models, as you will see later on in this chapter, are the foundation for the exponential smoothing forecasting models.

The simple moving average

The simple moving average (SMA) function, which we used in Chapter 5, Decomposition of Time Series Data, for smooth time series data can be utilized, with some simple steps, as a forecasting...

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