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.
Extracting Seasonality and Trend from Data: Decomposition Using R
Posted by Armando Brito Mendes | Filed under estatística, Investigação Operacional, lições, linguagens de programação, materiais ensino, materiais para profissionais
Uma excelente descrição da decomposição clássica com Python e R.
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.
Tags: engenharia, inferência, otimização, previsão
Voronoi diagram from smooshing paint between glass
Posted by Armando Brito Mendes | Filed under Investigação Operacional, matemática, SIG's, videos
Uma abordagem original aos diagramas de Voronoi.
Tags: belo, otimização
Data Analysis Method: Mathematics Optimization to Build Decision Making
Posted by Armando Brito Mendes | Filed under data mining, Investigação Operacional, matemática, SAD - DSS
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.
Tags: análise de dados, otimização
Markov Chains explained visually
Posted by Armando Brito Mendes | Filed under Investigação Operacional, matemática, materiais ensino, visualização
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.
Tags: grafos, otimização
IFORS Simulation
Posted by Armando Brito Mendes | Filed under estatística, Investigação Operacional, materiais ensino
The following 6 pages are in this category, out of 6 total.
D
E
M
S
T
Tags: otimização, software de otimização
IFORS Queueing_Theory
Posted by Armando Brito Mendes | Filed under Investigação Operacional, materiais ensino
The following 14 pages are in this category, out of 14 total.
Tags: otimização, software de otimização
IFORS Network_Flow_Problems
Posted by Armando Brito Mendes | Filed under Investigação Operacional, materiais ensino, planeamento
The following 10 pages are in this category, out of 10 total.
ACFG |
IMN |
N cont.T |
Tags: grafos, otimização, software de otimização
IFORS Linear_Programming
Posted by Armando Brito Mendes | Filed under Investigação Operacional, materiais ensino
The following 16 pages are in this category, out of 16 total.
DL |
L cont. |
MORT |
Tags: otimização, software de otimização
IFORS Education Resources Project
Posted by Armando Brito Mendes | Filed under estatística, Investigação Operacional, SAD - DSS, software
Welcome to the International Federation of Operational Research Societies (IFORS) Education Resources Project
- Main Page (19:13, 3 December 2013)
- Biased Random-Key Genetic Algorithms: A Tutorial (21:57, 2 December 2013)
- The Discrete Event System Specification Formalism (19:59, 2 December 2013)
- Urban Operations Research (01:36, 2 December 2013)
- Stochastic Models for Design and Planning (01:34, 2 December 2013)
- Queueing Theory Books Online (01:31, 2 December 2013)
- Practical Queueing Theory in Java (01:31, 2 December 2013)
- Explore Queueing Theory for Scheduling, Resource Allocation and Traffic Flow Applications (01:28, 2 December 2013)
- Stochastic Processes Course Notes (01:26, 2 December 2013)
- Test Problems for Non-Linear Programming (01:23, 2 December 2013)
- OR Notes: Separable Programming (01:21, 2 December 2013)
- OR Notes: Non-Linear Programming (01:20, 2 December 2013)
Tags: decisao em grupo, decisão médica, otimização, previsão, problemas, programação em folha de cálculo, software de otimização, software estatístico
Engineer solves Big Data Conjecture
Posted by Armando Brito Mendes | Filed under data mining, Investigação Operacional, matemática
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.
Tags: big data, otimização