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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
Author (1):
Rami Krispin Rami Krispin
Profile icon 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

Forecasting with Exponential Smoothing Models

In Chapter 5, Decomposition of Time Series Data, we looked at the application of smoothing functions for noise reduction in time series data and trend estimation. In this chapter, we will expand on the use of smoothing functions and introduce their forecasting applications. This family of forecasting models can handle a variety of time series types, from series with neither trends nor seasonal components to series with both trends and seasonal components. We will start with the basic moving average model and simple exponential smoothing models, and then add more layers to the model, as well as the model's ability to handle complex time series data.

In this chapter, we will cover the following topics:

  • Forecasting with moving average models
  • Forecasting approaches with smoothing models
  • Tuning parameters for smoothing models
...
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