Extracting Seasonality and Trend from Data: Decomposition Using R

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Uma excelente descrição da decomposição clássica com Python e R.

Time series decomposition works by splitting a time series into three components: seasonality, trends and random fluctiation. To show how this works, we will study the decompose( ) and STL( ) functions in the R language.

Understanding Decomposition

Decompose One Time Series into Multiple Series

Time series decomposition is a mathematical procedure which transforms a time series into multiple different time series. The original time series is often split into 3 component series:

  • Seasonal: Patterns that repeat with a fixed period of time. For example, a website might receive more visits during weekends; this would produce data with a seasonality of 7 days.
  • Trend: The underlying trend of the metrics. A website increasing in popularity should show a general trend that goes up.
  • Random: Also call “noise”, “irregular” or “remainder,” this is the residuals of the original time series after the seasonal and trend series are removed.

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MySQL Documentation

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Montes de documentação sobre todos os produtos MySQL

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C Tutorial

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Bom tutorial de C on-line.

Learn C with our popular C tutorial, which will take you from the very basics of C all the way through sophisticated topics like binary trees and data structures. By the way, if you’re on the fence about learning C or C++, I recommend going through the C++ tutorial instead as it is a more modern language.

Introduction and Basic C Features

Pointers, Arrays and Strings

File IO and command line arguments

Linked lists, binary trees, recursion

Finished with all these tutorials? Do some practice problems or view more tutorials.

A Growth Hacker’s Journey

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Uma história de vida muito interessante de um cientista dos dados \ hacker
Dr. Kirk Borne
Principal Data Scientist, Booz Allen Hamilton

Dr. Kirk Borne is a Principal Data Scientist at Booz Allen Hamilton. Previously he was a Professor of Astrophysics and Computational Science in the George Mason University School of Physics, Astronomy, and Computational Sciences. He was at Mason from 2003 to 2015, where he taught and advised students in the graduate and undergraduate Computational Science, Informatics, and Data Science programs. Before Mason, he spent nearly 20 years in positions supporting NASA projects, including an assignment as NASA’s Data Archive Project Scientist for the Hubble Space Telescope, and as Project Manager in NASA’s Space Science Data Operations Office. He has extensive experience in big data and data science, including expertise in scientific data mining and data systems. He has published over 200 articles (research papers, conference papers, and book chapters), and given over 200 invited talks at conferences and universities worldwide. In these roles, he focuses on achieving big discoveries from big data through data science, and he promotes the use of information and data-centric experiences with big data in the STEM education pipeline at all levels. He believes in data literacy for all! Learn more about him at http://kirkborne.net/. You can follow him on and on Twitter at @KirkDBorne, where he has been identified as one of the social network’s top big data influencers.

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