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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Voronoi diagram from smooshing paint between glass

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Uma abordagem original aos diagramas de Voronoi.

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Data Analysis Method: Mathematics Optimization to Build Decision Making

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Uma pequena introdução à utilização de otimização na análise de dados

Optimization is a problem associated with the best decision that is effective and efficient decisions whether it is worth maximum or minimum by way of determining a satisfactory solution.

Optimization is not a new science. It has grown even since Newton in the 17th century discovered how to count roots. Currently the science of optimization is still evolving in terms of techniques and applications. Many cases or problems in everyday life that involve optimization to solve them. Lately much developed especially in the emergence of new techniques to solve the problem of optimization. To mention some, among others, conic programming, semi definite programming, semi infinite programming and some meta heuristic techniques.

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Markov Chains explained visually

Boa forma de perceber como funcionam as cadeias de Markov

Boa forma de perceber como funcionam as cadeias de Markov

Adding on to their series of graphics to explain statistical concepts, Victor Powell and Lewis Lehe use a set of interactives to describe Markov Chains. Even if you already know what Markov Chains are or use them regularly, you can use the full-screen version to enter your own set of transition probabilities. Then let the simulation run.

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IFORS Simulation

Algoritmos e Problemas de Simulação

Algoritmos e Problemas de Simulação

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IFORS Queueing_Theory

Algoritmos e Problemas Filas de Espera

Algoritmos e Problemas Filas de Espera

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IFORS Network_Flow_Problems

Algoritmos e Problemas de Redes

Algoritmos e Problemas de Redes

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IFORS Linear_Programming

Programação Linear, Simplex e complementos

Programação Linear, Simplex e complementos

The following 16 pages are in this category, out of 16 total.

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IFORS Education Resources Project

portal com materal sobre Investigação Operacional, otimização e SADs

portal com materal sobre Investigação Operacional, otimização e SADs

Welcome to the International Federation of Operational Research Societies (IFORS) Education Resources Project

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Engineer solves Big Data Conjecture

Um problema de combinatória em contexto de big-data

Um problema de combinatória em contexto de big-data

IBM Distinguished Engineer solves Big Data Conjecture

A mathematical problem related to big data was solved by Jean-Francois Puget, engineer in the Solutions Analytics and Optimization group at IBM France. The problem was first mentioned on Data Science Central, and an award was offered to the first data scientist to solve it.

Bryan Gorman, Principal Physicist, Chief Scientist at Johns Hopkins University Applied Physics Laboratory, made a significant breakthrough in July, and won $500. Jean-Francois Puget completely solved the problem, independently from Bryan, and won a $1,000 award.

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