Once the data has been cleaned and reformatted, one of the first steps of the analysis is to identify the structure of the series components. The decomposition of time series is a generic name for the process of separating a series into its components. This process provides insights into the structural patterns of the series. Typically, those insights utilize and identify the most appropriate approaches to handle the series, based on the aim of the analysis (for example, seasonality analysis, and forecasting). For example, if you identify in this process that the series has a strong seasonality pattern, you should select models that have the ability to handle this pattern. Although there are multiple decomposition methods, in this chapter, we will focus on the classical seasonal decomposition method, as most methods are based on a type of extension...
- Tech Categories
- Best Sellers
- New Releases
- Books
- Videos
- Audiobooks
Tech Categories Popular Audiobooks
- Articles
- Newsletters
- Free Learning
You're reading from Hands-On Time Series Analysis with R
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.
Read more about Rami Krispin
Unlock this book and the full library FREE for 7 days
Author (1)
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.
Read more about Rami Krispin