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.
Optimal Wordle Solutions
Posted by Armando Brito Mendes | Filed under Investigação Operacional, materiais para profissionais
Uma aplicação que utiliza um processo de pesquisa em árvore para resolver o jogo wordle
The game Wordle has a lot of speculation online about what is the “best” first word. If we are exploring optimal strategies to solve the original game in the least number of guesses, most of it is wrong.
For humans, almost all of these words are great! However for optimal strategies, we need to examine all of the guesses, not just the first word. It turns out, it’s possible to solve 99% of all puzzles in only 4 guesses or with an average of ~3.42 guesses per win, but not with most of the “best” words found online.
Try out my solver with the best strategies that have been found so far.
Jonathan Olson
Tags: jogo, otimização
Journal of Physics: Conference Series
Posted by Armando Brito Mendes | Filed under estatística, Investigação Operacional, matemática, refs bibliográficas
longa lista de artigos resultantes de conferências de Matemática e Física
The open access Journal of Physics: Conference Series (JPCS) provides a fast, versatile and cost-effective proceedings publication service.
Tags: artigos científicos
Map of Best Breweries in America
Posted by Armando Brito Mendes | Filed under Investigação Operacional, mapas SIG's, visualização
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Um mapa com as melhores produtoras de cerveja artesanal nos EUA e uma rota otimizada com algoritmos genéticos
RateBeer puts out a list every year for top 100 breweries in the world. The rankings are based on reviews, range across styles, and historical performance (and maybe a bit of subjectivity). RateBeer just published the list for 2018. Here’s a map of the 73 U.S.-based breweries.
Brewery Road Trip, Optimized With Genetic Algorithm
Now that we know where they are, let’s find out how to visit all of them in one go.
Tags: belo, grafos, mapas, otimização, R-software, SIG
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
Voronoi diagram from smooshing paint between glass
Posted by Armando Brito Mendes | Filed under Investigação Operacional, mapas SIG's, matemática, videos
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Uma abordagem original aos diagramas de Voronoi.
Tags: belo, otimização
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
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Tags: software de otimização
How signal processing can be used to identify patterns in complex time series
Posted by Armando Brito Mendes | Filed under estatística, Investigação Operacional
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Uso de técnicas de processamento de sinal em séries cronológicas
The trend and seasonality can be accounted for in a linear model by including sinusoidal components with a given frequency. However, finding the appropriate frequency for each sinusoidal component requires a little more digging. This post shows how to use fast Fourier transforms to find these frequencies.
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
Three classes of metrics: centrality, volatility, and bumpiness
Posted by Armando Brito Mendes | Filed under estatística, Investigação Operacional
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introduz uma nova classe de estatísticas para séries cronológicas: bumpiness
All statistical textbooks focus on centrality (median, average or mean) and volatility (variance). None mention the third fundamental class of metrics: bumpiness.
Here we introduce the concept of bumpiness and show how it can be used. Two different datasets can have same mean and variance, but a different bumpiness. Bumpiness is linked to how the data points are ordered, while centrality and volatility completely ignore order. So, bumpiness is useful for datasets where order matters, in particular time series. Also, bumpiness integrates the notion of dependence (among the data points), while centrality and variance do not. Note that a time series can have high volatility (high variance) and low bumpiness. The converse is true.
The attached Excel spreadsheet shows computations of the bumpiness coefficient r for various time series. It is also of interest to readers who wish to learn new Excel concepts such a random number generation with Rand, indirect references with Indirect, Rank, Large and other powerful but not well known Excel functions. It is also an example of a fully interactive Excel spreadsheet driven by two core parameters.
Finally, this article shows (1) how a new concept is thought of, (2) then a robust, modern definition materialized, and (3) eventually a more meaningful definition created based on, and compatible with previous science.
Tags: data mining, 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
