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.
Axes of evil: How to lie with graphs
Posted by Armando Brito Mendes | Filed under materiais ensino, materiais para profissionais, Sem categoria, visualização
Um blog com exemplos e links para outros sites.
As Mark Twain once said, “Never let the truth get in the way of a good story.” Here are a few techniques to hide those pesky numbers and tell the story you feel, not the one you can prove.
Don your handlebar mustache and practice your evil laugh — we’re going in.
A Beginner’s Guide to learn web scraping with python!
Posted by Armando Brito Mendes | Filed under lições, materiais ensino, materiais para profissionais, software
Boa descrição de web scraping com Python
Web Scraping with Python
Imagine you have to pull a large amount of data from websites and you want to do it as quickly as possible. How would you do it without manually going to each website and getting the data? Well, “Web Scraping” is the answer. Web Scraping just makes this job easier and faster.
In this article on Web Scraping with Python, you will learn about web scraping in brief and see how to extract data from a website with a demonstration. I will be covering the following topics:
Tags: Python
The Beautiful Hidden Logic of Cities
Posted by Armando Brito Mendes | Filed under mapas SIG's, materiais ensino, materiais para profissionais, visualização
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Padrões identificados em mapas de cidades.
After finishing my map of the most common road suffixes by length, I realized I could also map each individual road, colored by its suffix. This has led to the loveliest maps I’ve made.
Driving around your city, you’re probably somewhat aware of Avenues and Boulevards and Streets and Roads and so on. Here in Portland, at least, I know that Avenues run north-south and Streets run east-west. However, it’s hard to get an overall view of how all these road designations knit together. By coloring them, we can suddenly see a new, stunning view of what we normally take for granted.
Tags: captura de conhecimento, image mining, mapas
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
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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
IFORS Developing Countries OR Resources Website
Posted by Armando Brito Mendes | Filed under Investigação Operacional, materiais ensino, SAD - DSS
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Artigos e software relacionado com Investigação Operacional
Click below on required topic headings to access papers or click here to access International Abstracts in OR
- Agriculture
- Communication
- Community OR
- Current Events
- Education
- Energy
- Environment
- Health
- Industry
- Infrastructure
- Labor
- Law Enforcement
- Safety
- General Articles
- Finance
- Transportation
- Free OR Software
Tags: software de otimização
The 5 Computer Vision Techniques
Posted by Armando Brito Mendes | Filed under lições, materiais ensino
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Boa introdução ao tema da visão por computador
The 5 Computer Vision Techniques That Will Change How You See The World
Computer Vision is one of the hottest research fields within Deep Learning at the moment. It sits at the intersection of many academic subjects, such as Computer Science (Graphics, Algorithms, Theory, Systems, Architecture), Mathematics (Information Retrieval, Machine Learning), Engineering (Robotics, Speech, NLP, Image Processing), Physics (Optics), Biology (Neuroscience), and Psychology (Cognitive Science). As Computer Vision represents a relative understanding of visual environments and their contexts, many scientists believe the field paves the way towards Artificial General Intelligence due to its cross-domain mastery.
So what is Computer Vision?
Tags: data mining, machine learning, robot
How To Use Multivariate Time Series Techniques For Capacity Planning on VMs
Posted by Armando Brito Mendes | Filed under estatística, Investigação Operacional, materiais ensino
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Métodos multivariados para séries cronológicas com VMs
Capacity planning is an arduous, ongoing task for many operations teams, especially for those who rely on Virtual Machines (VMs) to power their business. At Pivotal, we have developed a data science model capable of forecasting hundreds of thousands of models to automate this task using a multivariate time series approach. Open to reuse for other areas such as industrial equipment or vehicles engines, this technique can be applied broadly to anything where regular monitoring data can be collected.
Tags: data mining, machine learning, previsão
Recurrent neural networks, Time series data and IoT – Part One
Posted by Armando Brito Mendes | Filed under estatística, Investigação Operacional, materiais ensino
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Utilização de redes neuronais para previsão de séries univariadas
RNNs are already used for Time series analysis. Because IoT problems can often be modelled as a Time series, RNNs could apply to IoT data. In this multi-part blog, we first discuss Time series applications and then discuss how RNNs could apply to Time series applications. Finally, we discuss applicability to IoT.
In this article (Part One), we present the overall thought process behind the use of Recurrent neural networks and Time series applications – especially a type of RNN called Long Short Term Memory networks (LSTMs).
Tags: data mining, machine learning, previsão
imagens criadas por campos vetoriais
Posted by Armando Brito Mendes | Filed under materiais ensino, software, visualização
This website allows you to explore vector fields in real time.
“Vector field” is just a fancy way of saying that each point on a screen has some vector associated with it. This vector could mean anything, but for our purposes we consider it to be a velocity vector.
Now that we have velocity vectors at every single point, let’s drop thousands of small particles and see how they move. Resulting visualization could be used by scientist to study vector fields, or by artist to get inspiration!
Learn more about this project on GitHub
Stay tuned for updates on Twitter.
With passion,
Anvaka
Tags: belo
Free Hadoop Tutorial: Master BigData
Posted by Armando Brito Mendes | Filed under lições, materiais ensino, software
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BigData is the latest buzzword in the IT Industry. Apache’s Hadoop is a leading Big Data platform used by IT giants Yahoo, Facebook & Google. This course is geared to make a Hadoop Expert.
What should I know?
This is an absolute beginner guide to Hadoop. But knowledge of 1) Java 2) Linux will help
Syllabus
| Tutorial | Introduction to BIG DATA: Types, Characteristics & Benefits |
| Tutorial | Hadoop Tutorial: Features, Components, Cluster & Topology |
| Tutorial | Hadoop Setup Tutorial – Installation & Configuration |
| Tutorial | HDFS Tutorial: Read & Write Commands using Java API |
| Tutorial | What is MapReduce? How it Works – Hadoop MapReduce Tutorial |
| Tutorial | Hadoop & Mapreduce Examples: Create your First Program |
| Tutorial | Hadoop MapReduce Tutorial: Counters & Joins with Example |
| Tutorial | What is Sqoop? What is FLUME – Hadoop Tutorial |
| Tutorial | Sqoop vs Flume vs HDFS in Hadoop |
| Tutorial | Create Your First FLUME Program – Beginner’s Tutorial |
| Tutorial | Hadoop PIG Tutorial: Introduction, Installation & Example |
| Tutorial | Learn OOZIE in 5 Minutes – Hadoop Tutorial |
| Tutorial | Big Data Testing: Functional & Performance |
| Tutorial | Hadoop & MapReduce Interview Questions & Answers |
Tags: big data
